In the UK approximately 75% of the population has access to a computer, if not at home, at school, college, library etc. Below you will find an extensive, although not exhaustive list of specialist software and equipment on this site. During the last few years, speech recognition software has come a very long way, for instance, 'Dragon Naturally Preferred' is rated independently to be 99% accurate and is so easy to use, you'll wish you changed years ago. Inclusive Technology is an excellent company that has one many awards for their work with dyslexia and other special needs with computers. I am including games in this section because many parents often see arcade type games as children just playing. Computers are now helping children (and adults) at some schools and with private tutors, to learn to speak properly.
The ‘sell or you’re fired’ mentality that traditionally permeated the sales world has made way for a more nurturing approach in recent years.
In the old days, salespeople were hired and trained, given a computer and a bunch of leads and then it was up to them to get out and sell, leaving them to understand the market and work out how to drive sales themselves.
These days, sales managers take a much more sophisticated approach to their staff, identifying the salesperson’s strengths so that they can be optimised, and also establishing their weaknesses, so that they can be attacked with training. Part of this is because of the changing demographic of the modern salesperson says Patrick Stakenas, former research director at Gartner, now CEO and president of Determine. Garry Mansfield, managing director of Outside In Sales and Marketing Ltd provides some examples of the ways in which training and coaching can help leaders address challenges relating to sales performance. Training and coaching working together can undoubtedly help to raise an individual’s performance and thereby raise the performance of the team as a whole. So how can you ensure that the training you deliver will successfully drive sales performance in your team?
But sometimes training is required because a particular skill has been identified that the employee needs to improve in order to drive up their overall sales performance. Mansfield explains: “Training for personal development is about the person and how to improve their performance. McPheat recommends setting up a benchmarking system to measure employee development against pre-agreed targets. But he adds: “Individuals must own their personal development and should be encouraged to prepare the investment case for any training they want to do. The proliferation of learning and development (L&D) technology has also had a significant influence on the way that training can now be delivered to sales teams. For the last two years or so technology can bring training knowledge and training consumption really close to the point of need. What’s more, because these services are in a digital channel, sales leaders can measure if their reps are actually using the recommendations and watching them and then measure downstream what impact it has had on the deal outcome.
Indeed, there has been a notable shift towards more coaching in sales teams in recent years, according to McPheat.
Performance change can be coached over a longer period of time and focused specifically around the individual’s performance relative to expectations. So how can you ensure that you’re a good sales coach – or that you task the right members of your team with coaching?
To drive the performance of salespeople long-term, clearly training and coaching both have an important role to play, and sales leaders should use them in tandem. The most effective sales teams use a combination of workshop-based training and ongoing internal coaching. All of this is vital given the changing sales landscape, where the new generation of sales people require a more nurturing approach. Speech training method with alternative proper pronunciation database - Lessac Technology, Inc. TECHNICAL FIELDThe present invention relates to voice training including in a preferred embodiment, instruction in color, such as emotion and various Lessac energies, and using speech recognition in color, such as emotion and particularly suited to be implemented on personal computer systems.BACKGROUNDIn 1964, a group of computer scientists marveled over the new computer being delivered into the computer center at the Courant Institute for Mathematical Sciences at New York University. Computers can be of great benefit to people who are dyslexic or have other Specific Learning Difficulties (SpLDa€™s).
I have only included a few of the most tried and tested ones which support reading, spelling, numeracy, writing skills and organisational and planning skills. A speech training aid computer displays the word it wants a student to say and listens to the way the student repeats the word. The challenge here is not that they likely can do what you need of them, but they don’t want to. So with that in mind, let’s take a closer look at best practices relating to training and coaching in the sales department. Coaching is normally done on a one-to-one basis, whereas training can often be from one-to-hundreds.
Sales leaders that have been fastidious with their recruitment processes will have developed a team resourcing plan with an associated 'performance framework' for each of the key roles, which highlights core activities, skills and behaviours, thereby enabling them to hire the right people based on the needs of the department, but also helping them with ongoing skills development activities. This is not sheep-dipping everyone though the same training because it might be a good idea.
They must convince you on the value of this training to the business and commit up front to the post-course practice before you sign off. Coaching is viewed as an ongoing support activity to ensure that sales reps are always maximising their capability,” he says. This approach eradicates the spikes and troughs in performance, the fluctuating motivation, and replaces it with a steady improvement, as well as generating longer-term team loyalty, and therefore lowering staff attrition.
But also because the job of the salesperson is tough and getting tougher – more and more markets are becoming homogeneous; businesses are facing more competition and customers are becoming more demanding.
It’s the overarching catalyst to improve sales performance and training, coaching and development are all vehicles to enable this to happen,” says McPheat.
It uses a microphone to receive audible sounds input by a user into a first computing device having a program with a database consisting of (i) digital representations of known audible sounds and associated alphanumeric representations of the known audible sounds, and (ii) digital representations of known audible sounds corresponding to mispronunciations resulting from known classes of mispronounced words and phrases. A speech training method according to claim 1 wherein the second proper pronunciation database includes an associated grapheme and pronunciation rule for each selected word and for each alternative phonetic sequence for the selected word.
A speech training method according to claim 2 wherein the alternative phonetic sequences comprise a phonetic sequence for each selected word together with a phonetic sequence for the next preceding word in context or for the next succeeding word in context or for both said next preceding and succeeding words. A speech training method according to claim 3 wherein the phonetic sequences for each selected word are stored in the database as a single pronunciation record for the selected word.
A speech training method according to claim 2 wherein the alternative phonemes and corresponding alternative graphemes for a selected word map to a single correct text form of the selected word. A speech training method according to claim 1 wherein the context of the selected word comprises the position of the selected word in a sequence of words uttered. A speech training method according to claim 1 wherein the first and second proper pronunciation databases are available for audio playback to the user through the audio output device. A speech training method according to claim 8 comprising generating the second proper pronunciation database by recording statistically representative samples of the phonetic sequences of combinations of words in a language reference dictionary pronounced in accordance with pronunciation rules wherein the pronunciation rules are dependent upon the selected word, and the relationship of the selected word to the beginning or following word, or both the beginning and following words, in a phrase to be spoken. A speech training method according to claim 9 wherein the statistically representative sample comprises sentence structures and pronunciation sequences and by the method comprises employing a computational linguistic analysis of beginnings and endings of words and by identifying combinations that are cognitively meaningful to draw the statistically representative sample.
A speech training method according to claim 9 wherein each word in the sample is pronounced by certified speech practitioners of a common method of voice use and training, optionally the Lessac system, the method of voice use and training having its own phonetics and pronunciation rules. A speech training method according to claim 11 wherein the spoken words in the sample are representative of words spoken by high pitched, normally pitched, low pitched, rapidy spoken, normally spoken and slowly spoken speakers, by male, female, young and old speakers and by speakers of normal pitch.
A speech training method according to claim 11 comprising including in the spoken words in the recorded sample a full range of audible frequencies including harmonics created by the voice, the audible frequencies optionally being in the range of from about 500 to about 20,000 Hz. A speech training method according to claim 11 comprising employing a certified speech practitioner speaker to listen to high-fidelity reproductions of the pronunciation of words for the second proper pronunciation database made by other certified speech practitioners. A speech training method according to claim 14 wherein the listening practitioner notes and removes words having unintended errors of pronunciation and removes preceding and following words having proper alternative pronunciations. A speech training method according to claim 8 wherein the first and second proper pronunciation databases comprise standard native language pronunciations, optionally standard American English pronunciations, of the phonetic sequences for the words in the databases. A speech training method according to claim 16 comprising further employing, stored in the data storage facility, a proper pronunciation dialect database comprising acoustic profiles, graphemes, and text for identifying correct alternative words and pronunciations of words according to a known dialect of the native language. A speech training method according to claim 16 wherein the proper pronunciation dialect database comprises a database of rules-based dialectic pronunciations according to the Lessac system of pronunciation and communication. A speech training method according to claim 16 comprising further employing, stored in the data storage facility, an alternative proper pronunciation dialect database comprising alternative phonetic sequences for a dialect where the pronunciation of a word is modified according to the word's position in a sequence of words. A speech training method according to claim 16 comprising further employing, stored in the data storage facility, a pronunciation error database of phonetic sequences, acoustic profiles, graphemes and text for correctly identifying alternative pronunciations of words according to commonly occurring errors of articulation by native speakers of the language. A speech training method according to claim 16 comprising further employing, stored in the data storage facility, a Lessac pronunciation error database of common mispronunciations according to the Lessac system of pronunciation and communication. A speech training method according to claim 16 comprising employing, stored in the data storage facility, an individual word mispronunciation database.
A speech training method according to claim 16 comprising employing, stored in the data storage facility, a database of common word mispronunciations occurring when speaking a sequence of words. A speech training method according to claim 24 wherein the computerized system comprises a standard language model, a common phoneme error model, an exercise word model, an exercise phrase model, an exercise word error model and an exercise phrase error model.
A speech training method according to claim 8 wherein the computerized system comprises the display screen and a training text displayable on the display screen wherein the training text includes both exercise word models and exercise phrase models wherein the exercise word models and the exercise phrase models are organized in groups having common mispronunciation characteristics. A speech training method according to claim 8 wherein the computerized system comprises at least one model selected from the group consisting of a standard language model, a common phoneme error model, an exercise word model, an exercise phrase model, an exercise word error model and an exercise phrase error model. A speech training method according to claim 24 wherein the computerized system comprises a speech recognition program capable of recognizing words, mispronunciations and phoneme errors, and comprising audio recordings of proper pronunciations, the speech recognition program being operative during speech training sessions employing with the inventive interactive program wherein if no pronunciation error has occurred twice consecutively, the computerized system leaves the speech training routine and functions as a voice recognition system.
A speech training method according to claim 8 wherein the computerized system comprises a speech recognition program capable of recognizing words, mispronunciations and phoneme errors, and comprising audio recordings of the user's pronunciations, plus speech sounds for proper pronunciations, the speech recognition program being operative during the speech training.
A speech training method according to claim 29 wherein the computerized system is loaded with a speech recognition engine, the speech recognition engine having text editing and speech training utilities and with databases of word models, phrase models, vocal recordings and error models, the computerized system optionally being a personal computer available to the user.
A speech training method according to claim 8 wherein the computerized system comprises a speech recognition engine and wherein speech training and speech recognition are integrated to provide smooth switching between training and recognition modes to enable speech recognition to proceed and be intermittently supplemented with speech training as improperly pronounced words are encountered. A speech training method as claimed in claim 31 wherein said computerized system provides the user speech training using Lessac System techniques. A speech training method as claimed in claim 32, wherein the computerized system instructs the user in the correct pronunciation of a phoneme in terms of the sound of a musical instrument. A method of speech training as claimed in claim 32, wherein the user is given the option of receiving speech training or training the program to recognize the user's speech pattern. Programs and databases recorded on a CD-ROM or on permanent electronic storage media wherein the programs and databases capable of implementing a speech training method according to claim 1.
A computerized speech training system comprising a data processing unit, random access volatile memory available to the data processing unit, an audio device to audibilize output from the computerized system to be heard by the user and, optionally, a display screen, the computerized system having access to a data storage facility and further comprising stored in the data storage facility, a software program and at least one database to implement a speech training method wherein the software program comprises procedures to effect a speech training method according to claim 1.
A speech training method according to claim 38 wherein the spoken words in the sample are representative of words spoken by high pitched, normally pitched, low pitched, rapidly spoken, normally spoken and slowly spoken speakers, by male, female, young and old speakers and by speakers of normal pitch. A speech training method according to claim 39 comprising employing a certified speech practitioner speaker to listen to high-fidelity reproductions of the pronunciation of words for the second proper pronunciation database, their recordings being made by other certified speech practitioners.
A speech training method according to claim 40 wherein the listening practitioner notes and removes words having unintended errors of pronunciation and removes preceding and following words having proper alternative pronunciations. A speech training method according to claim 38 comprising including in the spoken words in the recorded sample a full range of audible frequencies in the range of from about 500to about 20,000 Hz.


The machine was the latest introduction from Control Data Corporation, a Model CDC 6600, whose speed and memory capacity far outstripped the 7.094 K random access memory capacity of the now humble IBM 7094 that it replaced. Sinking battleships in alphabetical order is far more exciting than looking for X, Y, Z, etc. Some possible reasons might be that they don’t agree with it, they're too lazy or lack motivation. Coaching is more personalised and is focused on where you need to get to, where you are now and what you need to do to manage the gap; more personalised to the individual. Performance management and coaching conversations isolate an individual's performance gaps (against the performance framework) and identify gaps related to a lack of know-how. Traditionally, instructor-led training was crucial and was the only means that leaders had to communicate detail and complexity to their sales people.
You don’t have to be brilliant at the task yourself you just need to ask the right questions and listen. A sales rep will go back to the office fired up, more motivated and with a toolbox full of sales skills. If sales departments are to hit their targets, they need to optimise their staff performance now and for the long-term. The method is performed by receiving the audible sounds in the form of the electrical output of the microphone.
In a portent of things to come, they little suspected that IBM, within months, would obsolete the long heralded CDC 6600 with its IBM 360, a machine which, incredibly, had an unheard-of 360 K of RAM, all built with discrete components, a conservative move in the face of concerns about the reliability of the then-new integrated circuit technology.
However, there are a lot of Touch-Typing courses available, some very good for people with dyslexia others I am not so sure of. In my experience this can't be trained-out of a "won't do" person but it can potentially be coached IF the person is willing to accept that they need to change. Now, however, digital technologies have reduced the reliance on instructor-led training, as Tad Travis, a research director at Gartner, notes. But although sales performance will improve, over time it will revert back to where it was because there is no driving force to keep the fires burning. A particular audible sound to be recognized is converted into a digital representation of the audible sound.
This impressive machine came to be housed in a room about eighteen feet square, and surrounded by ten or so air conditioners necessary to keep the system from overheating and failing. If they refuse to accept that they need to change and are unwilling to explore this further then it is best to cut this potentially corrosive element from the team. You can bounce ideas off them and they can be the voice of reason when needed and also can give you a kick up the backside if you’re not being as good as you can be. The digital representation of the particular audible sound is then compared to the digital representations of the known audible sounds to determine which of those known audible sounds is most likely to be the particular audible sound being compared to the sounds in the database. A half dozen tape decks, nearly a meter across and as tall as a man, and several key punch machines, the size of garment outer table serving machines completed the installation.Thirty-five years later, changes in technology have been remarkable. In response to a determination of error corresponding to a known type or instance of mispronunciation, the system presents an interactive training program from the computer to the user to enable the user to correct such mispronunciation.
Tiny laptop computing devices fly at speeds a thousand times that of those early powerhouse computers and boast thousands of times the memory. Instead of huge reels of recording tape, hard disks with capacities on the order of eighteen GB are found in those same laptop computing devices. These devices, with their huge memory and computing capabilities, move as freely in the business world as people, under the arm, in a bag, or on the lap of a businessman flying across the ocean. The vast majority of all computing tasks involved computers talking to other computers, or otherwise communicating through use of electrical input signals whose characteristics are substantially absolutely determined.
In this respect, computers are completely unlike the humans which they serve, humans whose communications vary in almost infinite ways, regardless of the method of communication, be it voice, writing, or other means.
If computing is to continue to make progress, computers must become integrated into usual human communications modalities.And, indeed, this is already happening. From a slow start at becoming an important factor in the marketplace about a decade ago, speech recognition technology holds just such a promise. A human voice interface to a computer represents what should be probably the most ideal evolutionarily defined modality for-the human-computer communications interface.
While humans customarily write, gesture and, to a limited extent, use other communications modes, voice communication remains predominant. This is not surprising, insofar as speech has evolved in human beings probably for many millions of years. This is believed to be the case because even relatively primitive forms of life have fairly highly developed “speech” characteristics.
For example, much work has been done in the study of the use of sounds to communicate various items of information by whales. Likewise, scientists have identified and cataloged uniform global communications patterns in chimpanzees.In view of the highly natural nature of communication by speech, a direct result of its having evolved over such a large fraction of the history of the species, speech communications impose an extremely low level of cognitive overhead on the brain, thus providing a facile communications interface while allowing the brain to perform a number of other functions simultaneously. For example, people engaged in sports activities routinely combine complex physical tasks, situational analysis, and exchange of information through speech, sometimes simultaneously transmitting and receiving audible information, while doing all of these other tasks.Clearly, the mind is well adapted to simultaneously control other tasks while communicating and receiving audible information in the form of speech. It is thus no surprise that virtually every culture on earth has devised its own highly sophisticated audible language.In view of the above, it is thus, easily understood why voice recognition technology has come to be the Holy Grail of computing. While useful work began to be done with this technology about ten years ago, users only obtained performance which left much to hope for. Individual quirks, regional pronunciations, speech defects and impediments, bad habits and the like pepper virtually everybody's speech to some extent. Good speech recognition requires not only good technology, it also requires recognizable speech.Toward this end, speech recognition programs generally have an error correction dialog window which is used to train the system to the features of an individual user's voice, as will be more fully described below. The motivation behind such technique is apparent when one considers and analyzes the schemes typically used in speech recognition systems.Early on, speech recognition was proposed through the use of a series of bandpass filters.
These proposals grew out of the use of spectrographic analysis for the purpose of speaker identification. More particularly, it was discovered that if one made a spectral print of a person saying a particular word, wherein the x-axis represented time and y-axis represented frequency, with the intensity of sound at the various frequencies being displayed in shades of gray or in black and white, the pattern made by almost every speaker was unique, largely as a function of physiology, and speakers could be identified by their spectrographic “prints.” Interestingly enough, however, the very diversity which this technique showed suggested to persons working in the field the likelihood that commonalities, as opposed to differences, could be used to identify words regardless of speaker. Hence the proposal for a series of bandpass filters to generate spectrographs for the purpose of speech recognition.While such an approach was logical given the state of technology in the 1960s, the problems were also apparent.
Obtaining high-quality factors or “Q” in electrical filters comprising inductors and capacitors is extremely difficult at audio frequencies.This is due to a number of factors. First of all, obtaining resonance at these frequencies necessitates the use of large capacitors and inductors. In the case of inductances, large values of inductance are required, thus requiring large lengths of wire for the windings and, accordingly, high resistance.
The result is that the selectivity of the filters is extremely poor and the ability to separate different bandpasses is compromised. Finally, the approach was almost fatally flawed, from a mass-market standpoint, by the fact that these tuned electrical circuits were very large and mechanically cumbersome, as well as very expensive.However, in the late 1960's, electrical engineers began to model the action of electrical circuits in the digital domain. This work was done by determining, using classical analytic techniques, the mathematical characteristics of the electrical circuit, and then solving these equations for various electrical inputs.
In the 1970's, it was well understood that the emerging digital technology was going to be powerful enough to perform a wide variety of computing tasks previously defaulted to the analog world. Thus, it was inevitable that the original approaches to voice recognition through the concept of using banks of tuned circuits would eventually come to be executed in the digital domain.In a typical speech recognition system, an acoustic signal received by a microphone is input into a voice board which digitizes the signal.
The computer then generates a spectrogram which, for a series of discrete time intervals, records those frequency ranges at which sound exists and the intensity of sound in each of those frequency ranges. The spectrogram, referred to in the art as a token, is thus a series of spectrographic displays, one for each of a plurality of time intervals which together form an audible sound to be recognized. Each spectrographic display shows the distribution of energy as a function of frequency during the time interval. In a typical system, sampling rates of 6,000 to 16,000 samples per second are typical, and are used to generate about fifty spectrum intervals per second for an audible sound to be recognized.In a typical system, quantitative spectral analysis is done for seven frequency ranges, resulting in eight spectral parameters for each fiftieth of a second, or spectral sample period. While the idea that a spectral analysis over time can be a reliable recognition strategy may be counterintuitive given the human perspective of listening to envelope, tonal variation and inflection, an objective view of the strategy shows that exactly this information is laid out in an easy to process spectral analysis matrix.Based on the theoretical underpinnings of the above recognition strategy, development of a speech recognition system involves the input of vocabulary into the hard drive of a computer in the form of the above described spectral analysis matrix, with one or more spectral analysis matrices for each word in the vocabulary of the system. But this picture, while in principle correct, is an unrealistic simplification of the problem of speech recognition.After a database of word models has been input into the system, comparison of an audible sound to the models in the database can be used as a reliable means for speech recognition. Thus, for one speaker, a word they take a certain period of time, while for another speaker, the word they take a longer period of time. In addition, speakers may give different inflections, emphasis, duration and so forth to different syllables of a word in different ways, depending on the speaker. Even a single speaker will speak in different ways on different occasions.Accordingly, effective speech recognition requires normalization of spoken sounds to word and phrase models in the database.
In other words, the encoded received sound or token must be normalized to have a duration equal to that of the model. This technology is referred to as time aligning, and results in stretching out or compressing the spoken sound or word to fit it against the model of the word or sound with the objective of achieving the best match between the model and the sound input into the system.
Of course, it is possible to leave the sound unchanged and stretch or compresses the model.In accordance with existing technology, each of the spectral sample periods for the sound to be recognized are compared against the corresponding spectral sample periods of the model which is being rated.
The cumulative score for all of the sample periods in the sound against the model is a quality rating for the match.
In accordance with existing technology, the quality ratings for all the proposed matches are compared and the proposed match having the highest quality rating is output to the system, usually in the form of a computer display of the word or phrase.However, even this relatively complex system fails to achieve adequate quality in the recognition of human speech. Accordingly, most commercial systems, do a contextual analysis and also require or strongly recommend a period of additional training, during which the above matching functions are performed with respect to a preselected text. During this process, the model is appended to take into account the individual characteristics of the person training the system. Finally, during use, an error correction dialog box is used when the user detects an error, inputs this information into the system and thus causes the word model to become adapted to the user's speech. This supplemental training of the system may also be enhanced by inviting the user, during the error correction dialog to speak the word, as well as other words that may be confused with the word by the system, into the system to further train the recognition engine and data base.As is apparent from the above discussion, the development of speech recognition systems has centered on assembling a database of sound models likely to have a high degree of correlation to the speech to be recognized by the speech recognition engine. The first is the input of global information using one or more speakers to develop a global database.
The second method in which the database is assembled is the training of the database to a particular user's speech, typically done both during a training session with preselected text, and on an ad hoc basis through use of the error correction dialog window in the speech recognition program.In over fifty years of work, Arthur Lessac has developed a complete voice system reflecting, for the first time, the basic relationship between music and speech.
His discovery and development was done empirically but was related to much formal academic work. His work early linked an understanding of music and singing with voice theory and rests on his decision to make a radical departure from traditional methods of studying and teaching voice. Very early in his speech work, Lessac decided that teaching or learning by imitating others was insufficient and damaging.
He determined to develop a system of learning based upon sensation and feeling and kinesthetic feedback principles. This required extensive practical and formal study of the natural functioning of the body and the voice.During almost this same fifty—year period, music historians began to go beyond studies of the history of western classical music. Related anthropological, archeological, historic and music work began to provide some insight into the origins of speech and music. Since these origins were before the time of recorded history, little progress was made until a number of studies of still—existing primitive tribes.


No one has, as yet, described the whole relationship between music and speech as has Lessac. However, there are indications that recent studies would support his main thesis.Today no complete vocal system compares to the Lessac system. Operationally, there is the linking together within a word, sentence, paragraph or speech of the different sounds where different emphasis can vary meaning.
The connection between vowel sounds and music has long been recognized—though never in a phonetic system. However, the same connection between the functional characterisitics of consonants and musical instruments and between the relationship between speech and a musical score has never before been developed.Voice and speech theory and training today depends heavily upon the International Phonetic Alphabet (IPA). It does remove orthographic traps, and it provides the student with a guide to specific vowel and consonant sounds in other languages that are missing in his own, although even in this context it does little more than any other alphabet when the spelling of a given language—Spanish, for example—is simplified.
And, it includes too many vocal traps.A symbol from the IPA system maps all of the possible sounds of the language, separating out deviations due to regional genesis which do not discriminate meaning within the culture.
This symbol must then be learned or memorized in conjunction with a sound (thus, involving the ear) in order to be understood and then spoken.And, the IPA does not deal at all with the operational linking together of sounds within words, phrases and larger units of speech. Based on a direct transfer from numeric description to action, this method of learning leaves no room for intervening influences to dilute or interfere with the process. Each of the consonants reflects a musical instrument and involves both the sound and the image of the playing of the instrument—images of touch, rhythm, melody, size and subtlety.To understand the instrument means to understand not only the sound itself but also the kinesthetic feel of the way the instrument is played and the different uses to which it can be put. It is an aesthetic construction and functions as a physical image.In conventional voice and speech training, even when the habit is more or less automatic, the sight of a “T” or a “V” will prepare the tongue and gum—ridge of the lips to execute the action to produce the desired explosive or fricative sound, but the sound that comes out is often unanticipated, irregular, defective and undetected by the ear. The impression often is that there must be at least a half dozen ways of making the sound.In the Lessac Consonant System, the picture of a snare drum with a “T” written on the picture will, after one has been taught the aesthetics of a drum beat, bypass and cut through the complexities of tongue manipulation, the memories of imitation, the listening by ear, etc.
The student is not only make a perfect “T” sound but will thereby also know how to feel the drumbeats of the “K”, “P”, “D”, “B”, and “G” without any additional training. What is more, once the concept is clear, one can ask a deaf person, or a foreigner, whether Chinese or French, to feel an “R”—trombone, or a “V”—cello, or an “5”—sound effect, or a “CH”—cymbal.
The result has been shown to be clear and perfect every time without ear judgment, mental confusion, physical or physiological gymnastics, and unaffected by any previous cultural or sectional influences that might work against this articulation.Traditionally, the study of voice and speech is divided into different disciplines—voice for singing, voice for speech, diction, public speaking, therapy, etc. All voice and speech is basically musical with the difference between speaking and singing being a relative one.Traditionally, consonants have been thought of as “articulated” sounds—primarily important for intelligibility. The Lessac instrumental approach to consonants suggests a reversal of the relative position of vowels and consonants in singing and speaking. In singing, the vowels make the principal artistic contribution; the consonants a utilitarian one.
It is sometimes difficult to communicate, but when communicated, it contains a tremendous amount of information in a “chunked” and, therefore, memorized state. Through a special kind of learning, images chunk information.Many people on first understanding the Lessac voice theory assume that his use of musical instruments to teach consonants and his overall musical approach is simply a useful teacher's analogy—or, if they disagree with it, a “trick” of some kind.
However, studies of the origins of music suggest that the relationship between music and speech and, within that, between consonants and musical instruments appears to be a fundamental one.
In all cultures, the development of specific instruments and vocal sounds appears to have been an inter—related process.
Certain instruments were built to mirror the image or sound of the vocal instrument and certain vocal sounds were made to mirror pleasing instrumental images or sounds—such as, basic percussive sounds, the twang of a bow string or the tone of an early horn.The Lessac consonant system applied to English reflects the instruments of Western European culture and its symphony orchestra.
In accordance with the invention, speech recognition software is used with the objective of improving the speech patterns of persons using the software. For example, salesman, lawyers, store clerks, mothers dealing with children and many others rely heavily on oral communications skills to accomplish daily objectives.
Nevertheless, many individuals possess poor speaking characteristics and take this handicap with them to the workplace and throughout daily life.Perhaps even more seriously, speech defects, regionalisms, and artifacts indicative of social standing, ethnic background and level of education often hold back persons otherwise eminently qualified to advance themselves in life. For this reason, speech, as a subject, has long been a part of the curricula in many schools, although in recent years education in this area has become, more and more, relegated to courses of study highly dependent on good speaking ability, such as radio, television, motion pictures and the theater.Part of the problem here has been the difficulty of finding good voice instructors in the relatively high cost of the individualized instruction needed for a high degree of effectiveness in this area. In accordance with the invention, a specialized but highly effective speech training regimen is provided for receiving human language inputs in audio form to a microphone, analyzing the same in a personal computer and outputting alphanumeric guidance and aural pronunciation examples from the sound card and speakers associated with a personal computer.In accordance with a present invention voice training is performed on a first computing device using a microphone to receive audible sounds input by a user into a first computing device having a program with a database comprising (i) digital representations of known audible sounds and associated alphanumeric representations of the known audible sounds and (ii) digital representations of known audible sounds corresponding to mispronunciations resulting from known classes of mispronounced words and phrases. The method is performed by having the person being trained read a preselected text and translating the audible sounds into the form of the electrical output of a microphone being sent to a computer. A particular audible sound to be recognized is converted into a digital representation of the audible sound.The digital representation of the particular audible sound is then compared to the digital representations of a proper pronunciation of that audible sound to determine whether there is an error that results from a known type or instance of mispronunciation. 1 is a block diagram illustrating a voice recognition program in accordance with the method of the present invention;FIG. 3 is a block diagram illustrating a detail of an alternative embodiment of the voice recognition program in accordance with the method of the present invention;FIG. 3 of the voice recognition program in accordance with the method of the present invention; andFIG.
5 is a block diagram illustrating an alternative text to speech routine in accordance with the method of the present invention.DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTSReferring to FIG. In accordance with the inventive method 10, a voice and error model is generated using subroutine 12. Subroutine 12 comprises a number of steps which are performed at the site of the software developer, the results of which are sent, for example, in the form of a CD-ROM, other media or via the Internet, together with the software for executing voice recognition, to a user, as will be apparent from the description below. In accordance with the present invention, the inventive speech recognition method may be practiced on personal computers, as well as on more advanced systems, and even relatively stripped down lightweight systems, such as subnotebooks and even smaller systems provided at the same have systems for interfacing with and receiving the output of a microphone. It is noted that quality sound board electronics is important to successful operation of the method of the invention, and USB microphones are preferred.Development PhaseAt step 14 a database of word models is generated by having speakers speak the relevant words and phrases into a microphone connected to the sound board of the personal computer being used to generate the database. In accordance with the preferred embodiment of the invention, speakers who have been trained in proper speech habits are used to input words, phrases and sounds into the database at step 14. As the information is generated by the speakers speaking into microphones, attached to the sound boards in the computer, the information is digitized, analyzed and stored on the hard drive 16 of the computer.In accordance with the present invention, relatively common phoneme pronunciation errors are also input into the system at step 18.
In this specific context the term “phoneme” is used to mean the smallest sound, perhaps meaningless in itself, capable of indicating a difference in meaning between two words. The word “dog” differs from “cog” by virtue of a change of the phoneme “do” pronounced “daw” and “co” pronounced “cah.”Thus, at step 18, the model generating speaker can speak a database of common phoneme errors into the microphone attached to the sound board of the computer to result in input to the database stored on the hard drive 16 of the computer. However, it is preferred that the phoneme errors are spoken by persons who in various ways make the pronunciation error as part of their normal speech patterns.At step 20, the system is enhanced by the introduction into the database contained on hard drive 16 of a plurality of exercise word models, selected for the purpose of training the speech of a user of the system. The same are input into the system through the use of a microphone and sound board, in the same way that the database of the language model was input into the system.
This is because if a person makes a speech pronunciation error of a particular type, it is likely that the same speaker makes certain other errors which have common characteristics with other pronunciation errors in the group. For example, a person who mispronounces the word “them” to sound like “dem” is also likely to mispronounce the words “that,” “those,” and “these.”Exercise phrase models are input at step 22. The exercise word models and the exercise phrase models input into the system at steps 20 and 22, respectively are associated in groups having common mispronunciation characteristics.
The same are input into the system through the use of a microphone and sound board, in the same way that the database of the language model was input into the system.In addition, in accordance with the present invention, it is recognized that computer errors may result in misrecognition of a particular error, mistaken acceptance of a mispronunciation, or mistaken rejection of a proper pronunciation. Accordingly, during the database generation session during which properly pronounced exercise word models or exercise phrase models are input into the system at steps 20 and 22, audio recordings of the same are also stored on hard disk 16, to allow for playback of these proper pronunciations during use of the program by a person performing speech recognition using the program. This provides for an audible cue to the user and allows the user to monitor the reliability of the system during the voice recognition and speech training process of the present invention.In accordance with the invention, it is anticipated that there may be more than one mispronunciation associated with a particular word or phrase. Accordingly, at step 24, a plurality of typical mispronunciations are input into the system to create a database of exercise word error models on hard drive 16.
The same are input into the system through the use of a microphone and sound board, in the same way that the database of the language model was input into the system.Finally, the database of relatively common mispronunciation errors is completed at step 26 where the speaker generating that database speaks into the system to generate a plurality of exercise phrase error models. These error models are also input into the system through the use of a microphone and stored on hard drive 16.In accordance with a preferred embodiment of the invention, the input of audible sounds into the system to generate the word error models at step 24 and the exercise phrase error models at step 26 is done using a speaker or speakers who have the actual speech error as part of their normal speech patterns. The same is believed to achieve substantially enhanced recognition of speech errors, although the same is not believed to be necessary to a functioning system.In accordance with the preferred embodiment of the invention, the models stored on hard disk 16, and generated as described above may be recorded on a CD-ROM or other program carrying media, together with a voice recognition engine, such as that marketed by any one of a number of manufacturers such as IBM, Dragon Systems, and others. In accordance with a present invention, such a prior art speech recognition program may be used for both the purpose of recognizing words, recognizing mispronunciations and phoneme errors, together with the above described audio recordings of proper pronunciations, during speech training sessions with the inventive interactive program.In accordance with the invention, such software comprising the speech recognition engine, editing and training utilities, and database of word models, phrase models, vocal recordings, and error models may be supplied to the user for a one time fee and transported over a publicly accessible digital network, such as the Internet.
Alternatively, the software may be made available for limited use for any period of time with charges associated with each such use, in which case the software would never be permanently resident on the computer of a user.User Training ProgramWhen a user desires to use the inventive program, the software containing the program and the database is loaded into a personal computer and the student user is instructed to read a preselected text that appears on the screen of the computer.
Thus, words are spoken into a microphone coupled to the sound board of the computer, in order to input the speech into the computer in the manner of a conventional speech recognition program.More particularly, as discussed above, after the system has proceeded through the performance of steps 14, 18, 20, 22, 24 and 26, and the speech recognition engine, editing and training utilities added, the system proceeds at step 28 to receive, through a microphone, speech to be recognized from a user of the program who has loaded the speech recognition engine, editing and training utilities, and database of word models, phrase models, vocal recordings, and error models onto the user's personal computer.
In this respect, the operation of the voice training program of the present invention is substantially identical to speech recognition programs presently on the market except for the database additions. More particularly, at step 30, a conventional speech recognition algorithm is applied to recognize audible sounds as the words which they are meant to represent.The computer then outputs the recognized speech on the screen of the computer monitor, and the next phrase uttered by the user proceeds at step 30 through the speech recognition algorithm resulting in that speech also being displayed on the monitor screen. When the user notices that an error has occurred, he may use any one of a number of different techniques to bring up an error correction window at step 32.
For example, he may simply double-click on the error, or highlight the erroneous recognition and hit a key dedicated to presentation of the error correction window.User correction occurs at step 34. In typical programs, call up of the error correction window results in the presentation of a screen showing the highlighted word, and suggesting, through the use of a menu, a number of alternatives which may be selected for double-clicking, in order to correct the error. Once the user indicates the error, the system then proceeds at step 36 to determine whether the error made by the user is recognized as one of the speech errors recognized by the system.
In this manner, the system keeps track of the number of errors of a particular type for the user by storing them and tallying them in hard drive 16.In accordance with the present invention, it is contemplated that the speech training will not be triggered by a single mispronunciation. Instead, it is contemplated that the repeated instances of a single type of mispronunciation error will be tallied, and when a threshold of pronunciation errors for that error is reached in the tally, only then will speech training be proposed by the appearance on the screen of a prompt window suggesting speech training.
The same could take the form of a window having the words “The system has determined that it is likely that we can improve your speech by coaching you now. Standby to talk to the speech coach.” The screen may also have a headline above the question, such as “The coach wants to talk to you!” The screen will also have a button bar “OK” to given the user the opportunity to click on the “OK” to start a training session.
A button marked “Cancel” may also be included so the student may click on the “Cancel” button to delay the speech coaching session a limited amount of time or reading later.It is also noted that other combinations of events may be used to trigger training. For example, if the particular mispronunciation detected is a very well-defined one, such as the almost uniform tendency of some speakers to mispronounce the word “oil” as “earl”, the definiteness with which this error has been determined makes training relatively likely to be necessary, and the threshold for that error can be lowered to, for example, one instance of that error being detected. In other cases, or in the general case, one may wish to set the threshold at three, five or even ten instances of the error before the “The coach wants to talk to you!” screen is presented to the user of the system.Once a mispronunciation has been detected by the system, the error correction algorithm operates in a manner identical to the conventional speech recognition algorithm at step 30, except that error correction algorithm checks the database of common phoneme errors input into the system by the software developer at step 18 and the exercise word error models and exercise phrase error models input at steps 24 and 26. In connection with this, it is noted that the so-called phoneme errors relate to particular sounds consisting of one syllable or less, while the phrase and word models are somewhat more general, as described herein.Thus, if, at step 42 the system determines that the threshold number of errors in that class has not been reached, it sends the system back to step 28, where speech recognition proceeds. If, on the other hand, a predetermined number of errors of the same class have been detected by the system and logged at step 40, at step 42 the system is sent to step 44 where the above described “The coach wants to talk to you!” screen is presented to the user, who is thus given the opportunity to train his voice.If the user declines the opportunity to train at step 46, the system is returned to step 28, where, again, reading of the rest of the preselected pronunciation error detecting text proceeds.
This is an option which may not be given, forcing the student to train, if the triggering number of errors have been made.In the other case, at step 46, when the user decides to accept speech training, the system proceeds to step 48, where the determination is made as toward whether the particular error is an error in the pronunciation of a word or what is referred to herein as a phrase. This may mean two or more words, or the combination of one or more words and at least a syllable from another word, and most often the end of one word combined with the beginning of another word, following the tendency of natural speakers to couple sounds to each other, sometimes varying their stand-alone pronunciation. If, at step 48 the system determines that the mispronunciation is the mispronunciation of a word, the system is sent to step 50 where the system retrieves from memory words which have the same or similar mispronunciation errors.As noted above, these words have been stored in the system, not only in the form of alphanumeric presentations, but also in high-quality audio format.



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Comments

  1. 13.09.2014 at 19:27:43


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    Author: lya
  2. 13.09.2014 at 18:33:24


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    Author: SeXyGiRl