Most current leading edge speech recognition systems are based
on an approach called hidden Markov modeling (HMM). Traditional
HMMs make some false assumptions, e.g., that speech features
occurring at one time are uncorrelated, and independent of other
recently occuring features (even ten milliseconds earlier). SRI
has developed a hybrid neural network/hidden Markov model speech
recognizer that improves the accuracy of traditional HMM by
modeling correlations among simultaneously occuring speech
features and between current and recent features. More recent
work involved modeling longer-term correlations and developing
speaker adaptation approaches within this new framework.
Representative Publications
H. Sedarat, R. Khadem, H. Franco (1998),
Simplified Neural Network Architectures in a Hybrid system for Isolated Speech Recognition,
Submitted to the International Conference on Acoustics, Speech, and Signal Processing,
Seattle, WA.
H. Franco, V. Digalakis (1997),
Correlation Modeling in a Hybrid Neural Network Hidden Markov Model Speech Recognizer,
Submitted to IEEE Transactions on Speech and Audio Processing.
H. Franco, V. Digalakis (1995),
Temporal Correlation Modeling in a Hybrid Neural Network/Hidden MArkov Model Speech Recognizer,
Proceedings of the 4th European Conference of Speech Communication and Technology,
Madrid, Spain.
M. Weintraub, V. Abrash, H. Franco, M. Cohen (1995),
"SRI Telespot, An LVCSR Telephone Transcription and Wordspotting System, Version using Multi-Layer Perceptrons",
SRI Technical Report.
H. Franco, V. Abrash, M. Cohen (1995),
"Neural Net Trainer for SRI's Hybrid HMM/MLP Speech Recognition System",
SRI Technical Report.
H. Franco, V. Abrash, M. Cohen, A. Sankar, M. Weintraub (1994),
Hybrid HMM/MLP Speech Recognition,
ARPA Artificial Neural Network Technology 1994 Program Review, December 6-8, Key West, FL.
D. Rumelhart, M. Cohen, H. Franco, V. Abrash (1991),
Supplementing HMM Continuous Speech Recognition with Neural Network Word Spotting,
Proceedings of the Speech Research Symposium XI, Baltimore, MD.