Task independent wordspotting using decision tree based allophone clustering

Authors:
Richard C. Rose;Edward M. Hofstetter
Affiliations:
AT&T Bell Laboratories, Murray Hill, New Jersey;MIT Lincoln Lab, Lexington, MA
Venue:
ICASSP'93 Proceedings of the 1993 IEEE international conference on Acoustics, speech, and signal processing: speech processing - Volume II
Year:
1993

Citing 3
Cited 0

Context dependent modeling of phones in continuous speech using decision trees

HLT '91 Proceedings of the workshop on Speech and Natural Language
Some applications of tree-based modelling to speech and language

HLT '89 Proceedings of the workshop on Speech and Natural Language
CMU robust vocabulary-independent speech recognition system

ICASSP '91 Proceedings of the Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

Any application involving keyword spotting from continuous speech utterances requires that the wordspotter vocabulary be easily reconfigurable, allowing the wordspotting "task" to change frequently with time. This paper investigates techniques for task independent wordspotter training. Keyword sub-word acoustic hidden Markov models are trained from a very large speech corpus formed from subsets of the TIMIT and General English speech corpora. Decision tree based allophone clustering procedures are used to obtain subword units that are both sensitive to contextual variability and trainable from the available speech data. Task independent wordspotting performance is demonstrated on unconstrained conversational speech utterances.