Two-stage discriminant analysis for improved isolated-word recognition
- 24 March 2005
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 12, 709-712
- https://doi.org/10.1109/icassp.1987.1169548
Abstract
This paper describes a two-stage isolated word speech recognition system that uses a Hidden Markov Model (HMM) recognizer in the first stage and a discriminant analysis system in the second stage. During recognition, when the first-stage recognizer is unable to clearly differentiate between acoustically similar words such as "go" and "no" the second-stage discriminator is used. The second-stage system focuses on those parts of the unknown token which are most effective at discriminating the confused words. The system was tested on a 35 word, 10,710 token stress speech isolated word data base created at Lincoln Laboratory. Adding the second-stage discriminating system produced the best results to date on this data base, reducing the overall error rate by more than a factor of two.Keywords
This publication has 6 references indexed in Scilit:
- Isolated word recognition using a two-pass pattern recognition approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- A speaker-stress resistant HMM isolated word recognizerPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Multi-style training for robust isolated-word speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- The discriminative network: A mechanism for focusing recognition in whole-word pattern matchingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Cepstral domain talker stress compensation for robust speech recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech RecognitionBell System Technical Journal, 1983