Sony Corporation

Sony Electronics Inc

Other

Gustavo Hernandez Abrego

Xavier Menendez-Pidal

Lex S Olorenshaw

Lei Duan

Eugene Koontz

A system and method for implementing a refined dictionary for speech recognition includes a database analyzer that initially identifies first vocabulary words that are present in a training database and second vocabulary words that are not present in the training database. A relevance module then performs refinement procedures upon the first vocabulary words to produce refined short word pronunciations and refined long word pronunciations that are added to a refined dictionary. A consensus module compares the second pronunciations with calculated plurality pronunciations to identify final consensus pronunciations that are then included in the refined dictionary.

System and method for implementing a refined dictionary for speech recognition

A system and method for effectively performing speech recognition procedures includes enhanced demiphone acoustic models that a speech recognition engine utilizes to perform the speech recognition procedures. The enhanced demiphone acoustic models each have three states that are collectively arranged to form a preceding demiphone and a succeeding demiphone. An acoustic model generator may utilize a decision tree for analyzing speech context information from a training database. The acoustic model generator then effectively configures each of the enhanced demiphone acoustic models as either a succeeding-dominant enhanced demiphone acoustic model or a preceding-dominant enhanced demiphone acoustic model to accurately model speech characteristics.

Methodology for generating enhanced demiphone acoustic models for speech recognition

A system and method for effectively implementing an optimized language model for speech recognition includes initial language models each created by combining source models according to selectable interpolation coefficients that define proportional relationships for combining the source models. A rescoring module iteratively utilizes the initial language models to process input development data for calculating word-error rates that each correspond to a different one of the initial language models. An optimized language model is then selected from the initial language models by identifying an optimal word-error rate from among the foregoing word-error rates. The speech recognizer may then utilize the optimized language model for effectively performing various speech recognition procedures.

System and method for effectively implementing an optimized language model for speech recognition

A system and method for performing a refinement procedure to effectively implement a speech recognition dictionary for spontaneous speech recognition may include a problematic word identifier configured to divide vocabulary words from an initial speech recognition dictionary into problematic words and non-problematic words according to pre-defined identification criteria. A candidate generator may analyze the problematic words to produce one or more pronunciation candidates for each of the problematic words. An optimization module may then perform an optimization process for refining one or more pronunciation candidates according to certain optimization criteria to thereby generate optimized problematic pronunciations. A dictionary refinement manager may finally combine the optimized problematic pronunciations with non-problematic pronunciations of the non-problematic words to produce a refined speech recognition dictionary for use by the speech recognition system.

Methodology for performing a refinement procedure to implement a speech recognition dictionary

A system and method for utilizing speech recognition to efficiently perform data indexing procedures includes an authoring module that coordinates an authoring procedure for creating an index file that has pattern word sets corresponding to data objects stored in a memory of a host electronic device. The pattern word sets are generated with a speech recognition engine that transforms spoken data descriptions into text data descriptions for creating the pattern word sets. The pattern word sets are associated in the index file with data object identifiers that uniquely identify the corresponding data objects. A retrieval module manages a retrieval procedure in which the speech recognition engine converts a spoken data request into a text data request. The retrieval module compares the text data request and the pattern word sets to identify a requested object identifier for locating a requested data object from among the data objects stored in the memory of the host electronic device.

System and method for utilizing speech recognition to efficiently perform data indexing procedures

A system and method is provided that randomly generates text with a given structure. The structure is taken from a number of learning examples. The structure of training examples is captured by word classification and the definition of the relationships between word classes in a given language. The text generated with this procedure is intended to replicate the information given by the original learning examples. The resulting text may be used to better model the structure of a language in a stochastic language model.

Supervised automatic text generation based on word classes for language modeling

The present invention comprises a system and method for speech verification using an efficient confidence measure, and includes a speech verifier which compares a confidence measure for a recognized word to a predetermined threshold value in order to determine whether the recognized word is valid, where a recognized word corresponds to a word model that produces a highest recognition score. In accordance with the present invention, the foregoing confidence measure may be calculated using the recognition score for the recognized word and a pseudo filler score that may be based upon selected average recognition scores from an N-best list of recognition candidates.

System and method for speech verification using an efficient confidence measure

A system and method for performing speech recognition in cyclostationary noise environments includes a characterization module that may access original cyclostationary noise from an intended operating environment of a speech recognition device. The characterization module may then convert the original cyclostationary noise into target stationary noise which retains characteristics of the original cyclostationary noise. A conversion module may then generate a modified training database by utilizing the target stationary noise to modify an original training database that was prepared for training a recognizer in the speech recognition device. A training module may then train the recognizer with the modified training database to thereby optimize speech recognition procedures in cyclostationary noise environments.

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Sunnyvale, CA, United States of America

Gustavo Hernandez Abrego