Abstract
Hidden Markov modeling is extended to speaker-independent phone recognition. Using multiple codebooks of various linear-predictive-coding (LPC) parameters and discrete hidden Markov models (HMMs) the authors obtain a speaker-independent phone recognition accuracy of 58.8-73.8% on the TIMIT database, depending on the type of acoustic and language models used. In comparison, the performance of expert spectrogram readers is only 69% without use of higher level knowledge. The authors introduce the co-occurrence smoothing algorithm, which enables accurate recognition even with very limited training data. Since the results were evaluated on a standard database, they can be used as benchmarks to evaluate future systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Hidden Markov modelSpeech recognitionComputer scienceSpectrogramPhoneTIMITSmoothingPattern recognition (psychology)Linear predictive codingArtificial intelligenceCoding (social sciences)Markov modelMarkov chainMachine learningSpeech codingMathematicsStatistics
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Publication Info
- Year
- 1989
- Type
- article
- Volume
- 37
- Issue
- 11
- Pages
- 1641-1648
- Citations
- 931
- Access
- Closed
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Cite This
K.-F. Lee,
Hsiao-Wuen Hon
(1989).
Speaker-independent phone recognition using hidden Markov models.
IEEE Transactions on Acoustics Speech and Signal Processing
, 37
(11)
, 1641-1648.
https://doi.org/10.1109/29.46546
Identifiers
- DOI
- 10.1109/29.46546