Application of hidden Markov models for recognition of a limited set of words in unconstrained speech
- 13 January 2003
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 254-257
- https://doi.org/10.1109/icassp.1989.266413
Abstract
The authors present an algorithm based on hidden Markov models which can recognize a predefined set of vocabulary items spoken in the context of fluent speech. They show that for a vocabulary of five words, it is possible to correctly recognize 87.1% of keywords when they occur in fluent speech and are spoken over a long-distance telephone network. While this task is significantly easier than what is normally associated with keyword spotting in continuous speech, it does address an important problem that must be solved for successful deployment of speech-recognition technology.<>Keywords
This publication has 13 references indexed in Scilit:
- An investigation of the use of dynamic time warping for word spotting and connected speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Keyword recognition using template concatenationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- High performance connected digit recognition, using hidden Markov modelsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Some performance benchmarks for isolated work speech recognition systemsComputer Speech & Language, 1987
- Application of hidden Markov models to automatic speech endpoint detectionComputer Speech & Language, 1987
- A Segmentalk-Means Training Procedure for Connected Word RecognitionAT&T Technical Journal, 1986
- A Study on the Ability to Automatically Recognize Telephone-Quality Speech From Large Customer PopulationsAT&T Technical Journal, 1985
- On the Recognition of Isolated Digits From a Large Telephone Customer PopulationBell System Technical Journal, 1983
- Isolated and Connected Word Recognition--Theory and Selected ApplicationsIEEE Transactions on Communications, 1981
- Minimum prediction residual principle applied to speech recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1975