Speechworks International, Inc.

Nortel Networks Corporation

Jean-Guy Dahan

Etienne Barnard

Michael Stuart Phillips

Vishwa N Gupta

Michael J Metzger

Peter R Stubley

Jianxiong Wu

A method and apparatus are provided for automatically training or modifying one or more models of acoustic units in a speech recognition system. Acoustic models are modified based on information about a particular application with which the speech recognizer is used, including speech segment alignment data for at least one correct alignment and at least one wrong alignment. The correct alignment correctly represents a phrase that the speaker uttered. The wrong alignment represents a phrase that the speech recognition system recognized that is incorrect. The segment alignment data is compared by segment to identify competing segments and those that induced the recognition error. When an erroneous segment is identified, acoustic models of the phoneme in the correct alignment are modified by moving their mean values closer to the segment's acoustic features. Concurrently, acoustic models of the phoneme in the wrong alignment are modified by moving their mean values further from the acoustic features of the segment of the wrong alignment. As a result, the acoustic models will converge to more optimal values based on empirical utterance data representing recognition errors.

Method and apparatus for discriminative training of acoustic models of a speech recognition system

A method and apparatus are provided for automatically recognizing words of spoken speech using a computer-based speech recognition system according to a dynamic semantic model. In an embodiment, the speech recognition system recognizes speech and generates one or more word strings, each of which is a hypothesis of the speech, and creates and stores a probability value or score for each of the word strings. The word strings are ordered by probability value. The speech recognition system also creates and stores, for each of the word strings, one or more keyword-value pairs that represent semantic elements and semantic values of the semantic elements for the speech that was spoken. One or more dynamic semantic rules are defined that specify how a probability value of a word string should be modified based on information about external conditions, facts, or the environment of the application in relation to the semantic values of that word string. A speech recognition application, upon receiving the keyword-value pairs, instructs the speech recognizer to modify one or more of the probability values, according to the dynamic semantic rules. The dynamic semantic rules are applied to the word strings and the keyword-value pairs. The speech recognizer modifies one or more of the probability values, re-orders the word strings, and returns control to the application. As a result, the speech recognizer may adjust dynamically to a changing likelihood that a speaker uttered a particular utterance, as the application executes, depending on the context of the application and the external factors.

Dynamic semantic control of a speech recognition system

The invention relates to a method and an apparatus for recognising speech, more particularly to a speech recognition system and method utilising a speech recognition dictionary supplemented by a lexicon containing frequently occurring word sequences (orthographies). In typical speech recognition systems, the process of speech recognition consists of scanning the vocabulary database or dictionary by using a fast match algorithm to find the top N candidates that potentially match the input speech. In a second pass the N candidates are re-scored using more precise likelihood computations. The novel method comprises the introduction of a step in the search stage that consists of forcing the insertion in the list of N candidates entries selected from a lexicon containing frequently used orthographies to increase the probability of occurrence of certain text combinations.

Method and apparatus for performing speech recognition utilizing a

The invention relates to a method and an apparatus for adding a new entry to a speech recognition dictionary, more particularly to a system and method for generating transcriptions from multiple utterances of a given word. The novel method and apparatus automatically transcribes several training utterances into transcriptions without knowledge of the orthography of the word being added. It also provides a method and apparatus for transcribing multiple utterances into a single transcription that can be added to a speech recognition dictionary. In a first step, each utterance is analyzed individually to get their respective acoustic characteristics. Following this, these characteristics are combined to generate a set of the most likely transcriptions using the acoustic information obtained from each of the training utterances.

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Brookline, MA, United States of America

Jean-Guy Dahan