Texas Instruments Corporation

Joseph W Picone

Barbara J Wheatley

George R Doddington

Charles T Hemphill

A voice log-in system is based on a person's spoken name input only, using speaker-dependent acoustic name recognition models in a performing speaker-independent name recognition. In an enrollment phase, a dual pass endpointing procedure defines both the person's full name (broad endpoints), and the component names separated by pauses (precise endpoints). An HMM (Hidden Markov Model) recognition model generator generates a corresponding HMM name recognition model modified by the insertion of additional skip transitions for the pauses between component names. In a recognition/update phase, a spoken-name speech signal is input to an HMM name recognition engine which performs speaker-independent name recognition--the modified HMM name recognition model permits the name recognition operation to accommodate pauses between component names of variable duration.

Voice log-in using spoken name input

A name recognition system (FIG. 1 )used to provide access to a database based on the voice recognition of a proper name spoken by a person who may not know the correct pronunciation of the name. During an enrollment phase (10), for each name-text entered (11) into a text database (12), text-derived recognition models (22) are created for each of a selected number of pronunciations of a name-text, with each recognition model being constructed from a respective sequence of phonetic features (15) generated by a Boltzmann machine (13). During a name recognition phase (20), the spoken input (24,25) of a name (by a person who may not know the correct pronunciation) is compared (26) with the recognition models (22) looking for a pattern match--selection of a corresponding name-text is made based on a decision rule (28).

Voice recognition of proper names using text-derived recognition models

A chart parser is disclosed which incorporates rule and observation probabilities with stochastic unification grammars. The parser operates frame synchronously to provide top-down hypotheses and to incorporate observation probabilities as they become available. Because the language model produces multiple explanations of the speech data between frames, the prediction and combination of rules may create cycles in a graph representing the best scores. Score calculation includes the detection of these cycles and propagation of the best scores to the next frame. The algorithm creates no more states than a nonprobilistic chart parser, and remains linear for regular grammars and cubic in the worst case for CFGs. The parser allows a direct integration of statistical speech information and linguistic constraints within the same language model, while the language model permits a generalization of HMM-type models. The efficiency of the parser makes it applicable to multiple levels of a spoken language system (e.g., sentence, word, phoneme, and phone levels).

Chart parser for stochastic unification grammar

A speech encoder is disclosed quantizing speech information with respect to energy, voicing and pitch parameters to provide a fixed number of bits per block of frames. Coding of the parameters takes place for each N frames, which comprise a block, irrespective of phonemic boundaries. Certain frames of speech information are discarded during transmission, if such information is substantially duplicated in an adjacent frame. A very low data rate transmission system is thus provided which exhibits a high degree of fidelity and throughput.

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Plano, TX, United States of America

Joseph W Picone