Abstract: A system for, and method of, generating an acoustic model and a mobile communication device that includes an acoustic model having at least one mixture weight vector generated by the method. In one embodiment, the method includes: (1) generating at least one mixture weight vector, (2) re-ordering elements of the at least one mixture weight vector to yield at least one re-ordered mixture weight vector and (3) vector quantizing the at least one re-ordered mixture weight vector to yield at least one quantized re-ordered mixture weight vector.

Abstract: A method for training acoustic models for a new target language is provided using a phonetic table, which characterizes the phones, used in one or more reference language(s) with respect to their articulatory properties; a phonetic table, which characterizes the phones used in the target language with respect to their articulatory properties; a set of trained monophones for the reference language(s); and a database of sentences in the target language and its phonetic transcription. With these inputs, the new method completely and automatically takes care of the steps of monophone seeding and triphone clustering and machine intensive training steps involved in triphone acoustic training.

Abstract: An improved method of providing out-of-vocabulary word rejection is achieved by adapting an initial garbage model (23) using received incoming enrollment speech (21).

Abstract: The method and system of adapting speech recognition models to a speaker environment may comprise receiving a spoken password (52) and getting a set of speaker independent (SI) speech recognition models (54). A mapping sequence may be determined for the spoken password (56). Using the mapping sequence, a speaker ID may be identified (58). A transform may be determined (66) between the SI speech recognition models and the spoken password using the mapping sequence. Speaker adapted (SA) speech recognition models may be generated (68) by applying the transform to SI speech recognition models. A speech input may be recognized (70) by applying the SA speech recognition models.

Abstract: This is a speech recognition method for modeling adjacent word context, comprising: dividing a first word or period of silence into two portions; dividing a second word or period of silence, adjacent to the first word, into two potions; and combining last portion of the first word or period of silence and first portion of the second word or period of silence to make an acoustic model. The method includes constructing a grammar to restrict the acoustic models to the middle-to-middle context.

Abstract: This invention consists of three enhancements to HMM-based automatic language identification systems. The three enhancements are: (i) language-discriminant acoustic model training and recognition, (ii) an acoustic model pruning procedure that picks only those phonetic models which are considered useful for language identification, and (iii) a neural network-based language classification method that uses knowledge-based features derived from phone sequences output by the HMM phonetic recognizers.

Abstract: An apparatus and method for verifying the identity of an unknown user of a telephone calling card service is disclosed which receives identification information corresponding to a valid user of the service, requests the unknown user to speak an authorization phrase without disclosing the authorization phrase to the unknown user, receives the unknown user's speech signal in response to the request, and verifies the identity of the unknown user based on the received speech signal. Other apparatus, systems, and methods are also disclosed.

Abstract: A speaker voice verification system uses temporal decorrelation linear transformation and includes a collector for receiving speech inputs from an unknown speaker claiming a specific identity, a word-level speech features calculator operable to use a temporal decorrelation linear transformation for generating word-level speech feature vectors from such speech inputs, word-level speech feature storage for storing word-level speech feature vectors known to belong to a speaker with the specific identity, a word-level speech feature vectors received from the unknown speaker with those received from the word-level speech feature storage, and speaker verification decision circuitry for determining, based on the similarity score, whether the unknown speaker's identity is the same as that claimed. The word-level vector scorer further includes concatenation circuitry as well as a word-specific orthogonalizing linear transformer. Other systems and methods are also disclosed.

Abstract: A speaker verification system receives input speech from a speaker of unknown identity. The speech undergoes linear predictive coding (LPC) analysis and transformation to maximize separability between true speakers and impostors when compared to reference speech parameters which have been similarly transformed. The transformation incorporated a "inter-class" covariance matrix of successful impostors within a database.