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EnglishAutomatic speech recognition (ASR) systems based on hidden Markov models (HMMs) are effective under many circumstances, but do suffer from some major limitations that limit their applicability in real-world environments. In particular, the 'local stationarity' requirement, implicit in standard HMMs, appears to be constraining. This paper reviews the major concepts underlying HMMs for ASR, pointing out a number of alternatives to enhance the basic paradigm in order to overcome the local-stationarity problem. Alternatives considered herein are: (i) introduction of dynamic acoustic features; (ii) definition of contest-dependent acoustic units; (iii) integration of segmental information; (iv) combination of artificial neural networks within hybrid HMM/connectionist architectures
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| Titolo: | HMMs for Acoustic Modeling: Beyond the Problem of Local Stationarity | |
| Autori: | ||
| Data di pubblicazione: | 1998 | |
| Abstract: | Automatic speech recognition (ASR) systems based on hidden Markov models (HMMs) are effective under many circumstances, but do suffer from some major limitations that limit their applicability in real-world environments. In particular, the 'local stationarity' requirement, implicit in standard HMMs, appears to be constraining. This paper reviews the major concepts underlying HMMs for ASR, pointing out a number of alternatives to enhance the basic paradigm in order to overcome the local-stationarity problem. Alternatives considered herein are: (i) introduction of dynamic acoustic features; (ii) definition of contest-dependent acoustic units; (iii) integration of segmental information; (iv) combination of artificial neural networks within hybrid HMM/connectionist architectures | |
| Handle: | http://hdl.handle.net/11582/1771 | |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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