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Confidence scoring for accurate HMM-based word recognition by using SM-based monophone score normalization
- Source :
- ICASSP
- Publication Year :
- 2002
- Publisher :
- IEEE, 2002.
-
Abstract
- In this paper, we propose a novel confidence scoring method that is applied to N-best hypotheses output from an HMM-based classifier. In the first pass of the proposed method, the HMM-based classifier with monophone models outputs N-best hypotheses and boundaries of all the monophones in the hypotheses. In the second pass, an SM(sub-space method)-based verifier tests the hypotheses by comparing confidence scores. We discuss how to convert a monophone similarity score of SM into a likelihood score, how to normalize the variations of acoustic quality in an utterance, and how to combine an HMM-based likelihood of word level and an SM-based likelihood of monophone level. In the experiments performed on speaker-independent word recognition, the proposed confidence scoring method significantly improves correct word recognition rate from 95.3% obtained by the standard HMM classifier to 98.0%.
Details
- Database :
- OpenAIRE
- Journal :
- IEEE International Conference on Acoustics Speech and Signal Processing
- Accession number :
- edsair.doi...........b47a34227a0bec09d61761df2624ffe5