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Easy Voice Training

 Project “Easy Voice Training” is based on a unique technology of audio signal comparison developed by Israeli  company Hineni Computer Self-Made Technology ltd. (HNNY) for usage in the fields of accent-independent speaker recognition and/or speech recognition. 

Goal: Creating tools for visual feedback from various vocal exercises. For example, improving accent, singing, pronunciation of certain phonemes like R, L, S, Sh, especially in cases where the person's audial feedback is impaired. For example: Persons suffering from lack of musical hearing (or memory), impaired hearing, those with difficulties of pronouncing foreign phonemes (i.e. the differences between S/Sh or R/L are difficult to perceive by Chinese speakers), children/adults with dyslalia or other speech impairment. In addition, it can be an aid for persons studying tonal languages (Chinese, Japanese) – languages which rely on changes in tone (pitch) for distinguishing different words or their inflections. 

The product: A set of applications (for PC and mobile devices) in the form of learning games, which would display results of real-time voice analysis and thus allow to adjust one's voice in real time and memorize the correct position and tension of vocal chords, tongue, lips etc. As well as a client-server network, providing the users with individually adjusted sets of exercises – automatically generated by the server, based on the user’s achievements sent from the client device. 

Method of operation: The incoming audio signal (phoneme, word, phrase) undergoes a sophisticated comparison with a defined standard.

The results of this comparison are:

1.       Finding matching parts of the sounds.

2.       Evaluating the rate of similarity between corresponding parts.

3.       Evaluating the ratio of matching parts' duration to whole stream duration.

4.       Calculating the difference vector. 

Matches/deviations from the standard are represented as a vector, composed of the rate of deviation and its direction. Example: for a Chinese speaker working on pronunciation of the SH sound, it may be a deflection towards the sounds S, Z or J. 

Differences from existing systems: Virtually all of comparable tools are based on the «listen and repeat» method and are useless for people with hearing impairment or difficulties. 

There exist absolutely no tools for learning tonal languages with visual feedback. Singing tutor programs (programs focused on producing correct pitch and duration of notes) have little to none visual feedback. 

The few (less than 5) existing tools for improving pronunciation (of English only) are based on speech recognition techniques and are accent-dependent.

These tools are absolutely useless for persons with hearing impairment as they don't evaluate the deviation from standard. Also, they are ineffective for Chinese or Japanese speakers, who don't perceive the differences between S and SH or R and L.


·         Learning tonal languages — Chinese/Japanese.

·         Treatment of speech disorders.

·         Practice of speech in persons with partial or total hearing loss (the US National Association of the Deaf counts at least as many as 1 million members).

·         Improving musical hearing and memory.

·         Accent correction. 

Audience: Various language courses – not depending on particular language, as the system is oriented at correcting pronunciation against an external standard and can be easily adapted to any course and method. Speech therapists. Amateur singers. Organizations, which monitor audio information exchange. Dedicated networks for pronunciation training with unlimited global access. (A 2003-2005 poll shows that over 1.5 million Chinese are willing to pay 5-10 USD every month for access to unlimited English pronunciation training resource. The estimated cost of launching such a network is at most 1 million USD and monthly maintenance costs are around 200 thousand USD, while the estimated monthly income exceeds 7.5 million USD.)

 State of the project: The algorithms and their implementation in code have been tested. The code can be ported to various platforms such as tablets, smartphones etc.

 Preparations are being made for creating a SDK for mobile platforms. Business model and function model of a full-scale (language-independent) training network are being developed.





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