Edwin R. Addison - Millersville MD, US H. Donald Wilson - White Plains NY, US Gary Marple - Boxborough MA, US Anthony H. Handal - Westport CT, US Nancy Krebs - Severn MD, US
Assignee:
Lessac Technology Inc. - White Plains NY
International Classification:
G10L015/26
US Classification:
704260, 704270
Abstract:
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.
Speech Training Method With Alternative Proper Pronunciation Database
Anthony H. Handal - Westport CT, US Gary Marple - Boxborough MA, US H. Donald Wilson - White Plains NY, US Michael Lessac - Beverly Hills CA, US
Assignee:
Lessac Technology, Inc. - White Plains NY
International Classification:
G10L015/04
US Classification:
704270, 704251, 704257, 704260
Abstract:
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.
Method Of Recognizing Spoken Language With Recognition Of Language Color
H. Donald Wilson - White Plains NY, US Anthony H. Handal - Westport CT, US Gary Marple - Boxborough MA, US Michael Lessac - Beverly Hills CA, US
Assignee:
Lessac Technologies, Inc. - Boxborough MA
International Classification:
G10L 15/04
US Classification:
704251
Abstract:
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.
Prosodic Speech Text Codes And Their Use In Computerized Speech Systems
Gary Marple - Boxborough MA, US Sue Ann Park - Newtown Square PA, US H. Donald Wilson - White Plains NY, US Mary Louise B. Wilson, legal representative - Bowie MD, US Nancy Krebs - Severn MD, US Diane Gaary - Ardmore PA, US Barry Kur - State College PA, US
Assignee:
Lessac Technologies, Inc. - Boxborough MA
International Classification:
G10L 13/08
US Classification:
704260
Abstract:
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.
System-Effected Text Annotation For Expressive Prosody In Speech Synthesis And Recognition
Rattima Nitisaroj - Shrewsbury MA, US Gary Marple - Boxborough MA, US Nishant Chandra - Shrewsbury MA, US
Assignee:
Lessac Technologies, Inc. - West Newton MA
International Classification:
G10L 19/00
US Classification:
704260, 704251, 704258
Abstract:
The inventive system can automatically annotate the relationship of text and acoustic units for the purposes of: (a) predicting how the text is to be pronounced as expressively synthesized speech, and (b) improving the proportion of expressively uttered speech as correctly identified text representing the speaker's message. The system can automatically annotate text corpora for relationships of uttered speech for a particular speaking style and for acoustic units in terms of context and content of the text to the utterances. The inventive system can use kinesthetically defined expressive speech production phonetics that are recognizable and controllable according to kinesensic feedback principles. In speech synthesis embodiments of the invention, the text annotations can specify how the text is to be expressively pronounced as synthesized speech. Also, acoustically-identifying features for dialects or mispronunciations can be identified so as to expressively synthesize alternative dialects or stylistic mispronunciations for a speaker from a given text.
Computerized Speech Synthesizer For Synthesizing Speech From Text
Gary Marple - Boxborough MA, US Nishant Chandra - Shrewsbury MA, US
Assignee:
Lessac Technologies, Inc. - West Newton MA
International Classification:
G10L 13/00 G10L 13/08
US Classification:
704260, 704258
Abstract:
Disclosed are novel embodiments of a speech synthesizer and speech synthesis method for generating human-like speech wherein a speech signal can be generated by concatenation from phonemes stored in a phoneme database. Wavelet transforms and interpolation between frames can be employed to effect smooth morphological fusion of adjacent phonemes in the output signal. The phonemes may have one prosody or set of prosody characteristics and one or more alternative prosodies may be created by applying prosody modification parameters to the phonemes from a differential prosody database. Preferred embodiments can provide fast, resource-efficient speech synthesis with an appealing musical or rhythmic output in a desired prosody style such as reportorial or human interest. The invention includes computer-determining a suitable prosody to apply to a portion of the text by reference to the determined semantic meaning of another portion of the text and applying the detennined prosody to the text by modification of the digitized phonemes. In this manner, prosodization can effectively be automated.
Methods Employing Phase State Analysis For Use In Speech Synthesis And Recognition
Nishant Chandra - Shrewsbury MA, US Reiner Wilhelms-Tricarico - Belchertown MA, US Rattima Nitisaroj - West Newton MA, US Brian Mottershead - West Newton MA, US Gary A. Marple - West Newton MA, US John B. Reichenbach - West Newton MA, US
Assignee:
Lessac Technologies, Inc. - West Newton MA
International Classification:
G10L 15/26
US Classification:
704235
Abstract:
A computer-implemented method for automatically analyzing, predicting, and/or modifying acoustic units of prosodic human speech utterances for use in speech synthesis or speech recognition. Possible steps include: initiating analysis of acoustic wave data representing the human speech utterances, via the phase state of the acoustic wave data; using one or more phase state defined acoustic wave metrics as common elements for analyzing, and optionally modifying, pitch, amplitude, duration, and other measurable acoustic parameters of the acoustic wave data, at predetermined time intervals; analyzing acoustic wave data representing a selected acoustic unit to determine the phase state of the acoustic unit; and analyzing the acoustic wave data representing the selected acoustic unit to determine at least one acoustic parameter of the acoustic unit with reference to the determined phase state of the selected acoustic unit. Also included are systems for implementing the described and related methods.
Edwin Addison - Millersville MD, US H. Wilson - White Plains NY, US Gary Marple - Boxborough MA, US Anthony Handal - Westport CT, US Nancy Krebs - Severn MD, US
International Classification:
G10L013/08
US Classification:
704/260000
Abstract:
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.
Vander Wyk & Burnham 2003 - 2009
Investor
Independent Book Publishers Association 2003 - 2009
Member
Arts|Learning 2003 - 2009
Trustee
Lessacinstitute 2003 - 2009
Member
Linden Hill School 1999 - 2005
Trustee
Education:
Mit Sloan School of Management 1962 - 1963
Michigan State University 1959 - 1962
Doctorates, Doctor of Business Administration, Economics, Social Psychology
Drake University 1956 - 1959
Bachelors, Bachelor of Science, Economics, Philosophy, Business
Skills:
Strategic Planning Start Ups Entrepreneurship Leadership Management Strategic Partnerships Business Strategy Management Consulting Venture Capital Integration Strategy Business Development Public Speaking Nonprofits Software Development Team Building Executive Management Saas Voice User Interface Design Books Enterprise Software Natural Language Processing Teaching Non Profits Publishing Knowledge Management Investments Speech Recognition Consulting Nuance
Joel Charles Jil Jennewein Gary Marple Class of 1987 David Bates Barbara Giese Class of 1984 Sharee Johnson Rick Pittenger Class of 1981 David Davidson Tom