Data processing: speech signal processing – linguistics – language – Speech signal processing – Recognition
Patent
1996-08-28
1999-01-05
Knepper, David D.
Data processing: speech signal processing, linguistics, language
Speech signal processing
Recognition
704221, G10L 504
Patent
active
058571698
ABSTRACT:
A time-sequential input pattern (20), which is derived from a continual physical quantity, such as speech is recognized. The system includes input means (30), which accesses the physical quantity and therefrom generates a sequence of input observation vectors. The input observation vectors represent the input pattern. A reference pattern database (40) is used for storing reference patterns, which consist of a sequence of reference units. Each reference unit is represented by associated reference probability densities. A tree builder (60) represents for each reference unit the set of associated reference probability densities as a tree structure. Each leaf node of the tree corresponds to a reference probability density. Each non-leaf node corresponds to a cluster probability density, which is derived from all reference probability densities corresponding to leaf nodes in branches below the non-leaf node. A localizer (50) is used for locating among the reference patterns stored in the reference pattern database (40) a recognised reference pattern, which corresponds to the input pattern. The locating includes, for each input observation vector, searching each tree structure for reference probability densities which give a high likelihood for the observation vector. Each tree is searched by selecting at the level immediately below the root node a number of nodes for which the corresponding cluster probability densities give an optimum cluster likelihood. This is repeated at successively lower levels of the tree by using each selected node as a root node, until the selected node is a leaf node. For each selected leaf node, the corresponding reference probability density is used to calculate the likelihood of the input observation vector. These likelihoods are combined per reference pattern to give a pattern similarity score. The recognised pattern is one of the reference patterns for which an optimum of the pattern similarity scores is calculated. Output means (70) are used for outputting the recognised pattern.
REFERENCES:
patent: 5528701 (1996-06-01), Aref
L. Rabiner "A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition", Proceeding of the IEEE, vol. 77, Feb. 1989.
L. Rabiner, "Fundamentals of Speech Recognition", Prentice Hall, Section 3.4.4, p. 125.
Fissore, Luciano et al. Lexical Access to Large Vocabularies for Speech Recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing. vol. 37, No. 8, Aug. 89. 1197-1213.
Foote, J.T. Discrete MMI probability models for HMM Speech Recognition. ICASSP '95: Acoustics, Speech and Signal Processing. May 9. 461-464.
Rabiner, L.R. HMM Clustering for Connected Word Recogntion. ICASSP '89: Acoustics, Spech and Signal Processing. Feb. 89. 405-408.
Watanabe, Takao. High Speed Speech Recognition Using Tree-Structured Proability Density Function. ICASSP '95: Acoustics, Speech and Signal Processing. May 95. 556-559.
Knepper David D.
Sofocleous M. David
U.S. Philips Corporation
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