Abstract: 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.

Abstract: 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.

Abstract: A system and method for speech recognition using an enhanced phone set comprises speech data, an enhanced phone set, and a transcription generated by a transcription process. The transcription process selects appropriate phones from the enhanced phone set to represent acoustic-phonetic content of the speech data. The enhanced phone set includes base-phones and composite-phones. A phone dataset includes the speech data and the transcription. The present invention also comprises a transformer that applies transformation rules to the phone dataset to produce a transformed phone dataset. The transformed phone dataset may be utilized in training a speech recognizer, such as a Hidden Markov Model. Various types of transformation rules may be applied to the phone dataset of the present invention to find an optimum transformed phone dataset for training a particular speech recognizer.

Abstract: The present invention comprises a methodology for implementing a vocabulary set for use in a speech recognition system, and may preferably include a recognizer for analyzing utterances from the vocabulary set to generate N-best lists of recognition candidates. The N-best lists may then be utilized to create an acoustical matrix configured to relate said utterances to top recognition candidates from said N-best lists, as well as a lexical matrix configured to relate the utterances to the top recognition candidates from the N-best lists only when second-highest recognition candidates from the N-best lists are correct recognition results. An utterance ranking may then preferably be created according to composite individual error/accuracy values for each of the utterances. The composite individual error/accuracy values may preferably be derived from both the acoustical matrix and the lexical matrix.

Abstract: The present invention comprises a methodology for implementing a vocabulary set for use in a speech recognition system, and may preferably include a recognizer for analyzing utterances from the vocabulary set to generate N-best lists of recognition candidates. The N-best lists may then be utilized to create an acoustical matrix configured to relate said utterances to top recognition candidates from said N-best lists, as well as a lexical matrix configured to relate the utterances to the top recognition candidates from the N-best lists only when second-highest recognition candidates from the N-best lists are correct recognition results. An utterance ranking may then preferably be created according to composite individual error/accuracy values for each of the utterances. The composite individual error/accuracy values may preferably be derived from both the acoustical matrix and the lexical matrix.

Abstract: A method and an apparatus for a parameter sharing speech recognition system are provided. Speech signals are received into a processor of a speech recognition system. The speech signals are processed using a speech recognition system hosting a shared hidden Markov model (HMM) produced by generating a number of phoneme models, some of which are shared. The phoneme models are generated by retaining as a separate phoneme model any triphone model having a number of trained frames available that exceeds a prespecified threshold. A shared phoneme model is generated to represent each of the groups of triphone phoneme models for which the number of trained frames having a common biphone exceed the prespecified threshold. A shared phoneme model is generated to represent each of the groups of triphone phoneme models for which the number of trained frames having an equivalent effect on a phonemic context exceed the prespecified threshold.