A Novel Uncertainty Decoding Rule With Applications to Transmission Error Robust Speech Recognition

In this paper, we derive an uncertainty decoding rule for automatic speech recognition (ASR), which accounts for both corrupted observations and inter-frame correlation. The conditional independence assumption, prevalent in hidden Markov model-based ASR, is relaxed to obtain a clean speech posterior...

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Veröffentlicht in:	IEEE transactions on audio, speech, and language processing Jg. 16; H. 5; S. 1047 - 1060
Hauptverfasser:	Ion, V., Haeb-Umbach, R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway, NJ IEEE 01.07.2008 Institute of Electrical and Electronics Engineers
Schlagworte:	Acoustic distortion Acoustic noise Applied sciences Automatic speech recognition Cleaning Coding, codes Communication networks Conditional independence Conditioning Correlation Decoding distributed speech recognition error concealment Exact sciences and technology Hidden Markov models Information, signal and communications theory network speech recognition Networks Robustness Signal and communications theory Signal processing Speech Speech processing Speech recognition Telecommunications and information theory Uncertainty uncertainty decoding Packet switching distributed speech recognition network speech recognition Probabilistic approach Data transmission Man machine dialogue Speech-based user interfaces Decoding Distributed system Codec Conditional independence Hidden Markov models Speech recognition Telecommunication network error concealment uncertainty decoding Error correction Transmission loss Automatic recognition Speech processing Internet telephony Transmission error
ISSN:	1558-7916
Online-Zugang:	Volltext
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