Acoustic-phonetic transformations for improved speaker-independent isolated word recognition
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 561-564 vol.1
- https://doi.org/10.1109/icassp.1991.150401
Abstract
The authors present a method for improving HMM (hidden Markov model) phonetic discrimination capability which is a linear discriminant transform of acoustic features to a continuous-valued feature space such that phonetic distinctions correlate closely with Euclidean distance in the transformed feature space. Experimental testing with a 30-word single syllable highly confusable vocabulary showed that the acoustic-phonetic transform could be used to reduce word error rates approximately 25%. In general, results based on the LDA2 transform, i.e., linear discriminant analysis with whitening of the within-class covariance matrices, are superior to those obtained with LDA1, linear discriminant analysis without whitening. Recognition results also improve if a block transform of several frames per block is used rather than a transform based on one frame per block.Keywords
This publication has 3 references indexed in Scilit:
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- A comparison of several acoustic representations for speech recognition with degraded and undegraded speechPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Discriminant analysis and supervised vector quantization for continuous speech recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002