Lessac Technology, Inc.

Lessac Technologies, Inc.

H Donald Wilson

Gary A Marple

Anthony H Handal

Nancy Krebs

Edwin Addison

Michael Lessac

Mary Louise B Wilson, Legal Representative

Diane Gaary

Sue Ann Park

Barry Kur

A method of, and system for, acoustically coding text for use in the synthesis of speech from the text. The method includes marking the text to be spoken with one or more graphic symbols to indicate to a speaker a desired prosody to impart to the spoken text. The markups can include grapheme-phoneme pairs each wherein a visible prosodic-indicating grapheme is employed with written text and a corresponding digital phoneme is functional in the digital domain. The invention is useful in the generation of appealing, humanized machine speech for a wide range of applications, including voice mail systems, electronically enabled appliances, automobiles, computers, robotic assistants, games and the like, in spoken books and magazines, drama and other entertainment.

Prosodic speech text codes and their use in computerized speech systems

In accordance with a present invention speech recognition is disclosed. 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. The method is performed by receiving the audible sounds in the form of the electrical output of the microphone. 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 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 speech recognition output consisting of the alphanumeric representation associated with the audible sound most likely to be the particular audible sound is then produced. An error indication is then received from the user indicating that there is an error in recognition. The user also indicates the proper alphanumeric representation of the particular audible sound. This allows assistant to determine whether the error is a result of a known type or instance of mispronunciation. 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.

Method of recognizing spoken language with recognition of language color

In accordance with a present invention speech training system is disclosed. 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. The method is performed by receiving the audible sounds in the form of the electrical output of the microphone. 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 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. 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.

Speech training method with alternative proper pronunciation database

A preferred embodiment of the method for converting text to speech using a computing device having a memory is disclosed. The inventive method comprises examining a text to be spoken to an audience for a specific communications purpose, followed by marking-up the text according to a phonetic markup systems such as the Lessac System pronunciation rules notations. A set of rules to control a speech to text generator based on speech principles, such as Lessac principles. Such rules are of the tide normally implemented on prior art text-to-speech engines, and control the operation of the software and the characteristics of the speech generated by a computer using the software. A computer is used to speak the marked-up text expressively. The step of using a computer to speak the marked-up text expressively is repeated using alternative pronunciations of the selected style of expression where each of the tonal, structural, and consonant energies, have a different balance in the speech, are also spoken to a trained speech practitioners that listened to the spoken speech generated by the computer. The spoken speech generated by the computer is then evaluated for consistency with style criteria and/or expressiveness. And audience is then assembled and the spoken speech generated by the computer is played back to the audience. Audience comprehension of spoken speech generated by the computer is evaluated and correlated to a particular implemented rule or rules, and those rules which resulted relatively high audience comprehension are selected.

Text to speech

A preferred embodiment of the method for converting text to speech using a computing device having a memory is disclosed. Text, being made up of a plurality of words, is received into the memory of the computing device. A plurality of phonemes are derived from the text. Each of the phonemes is associated with a prosody record based on a database of prosody records associated with a plurality of words. A first set of the artificial intelligence rules is applied to determine context information associated with the text. The context influenced prosody changes for each of the phonemes is determined. Then a second set of rules, based on Lessac theory to determine Lessac derived prosody changes for each of the phonemes is applied. The prosody record for each of the phonemes is amended in response to the context influenced prosody changes and the Lessac derived prosody changes. Then a reading from the memory sound information associated with the phonemes is performed. The sound information is amended, based on the prosody record as amended in response to the context influenced prosody changes and the Lessac derived prosody changes to generate amended sound information for each of the phonemes. Then the sound information is outputted to generate a speech signal.

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White Plains, NY, United States of America

H Donald Wilson