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
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Beschreibung
Zusammenfassung: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 that is conditioned on the complete observed feature vector sequence. This is a more informative posterior than one conditioned only on the current observation. The novel decoding is used to obtain a transmission-error robust remote ASR system, where the speech capturing unit is connected to the decoder via an error-prone communication network. We show how the clean speech posterior can be computed for communication links being characterized by either bit errors or packet loss. Recognition results are presented for both distributed and network speech recognition, where in the latter case common voice-over-IP codecs are employed.
Bibliographie:ObjectType-Article-2
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ISSN:1558-7916
DOI:10.1109/TASL.2008.925879