HMM-Based Reconstruction of Unreliable Spectrographic Data for Noise Robust Speech Recognition

This paper presents a framework for efficient HMM-based estimation of unreliable spectrographic speech data. It discusses the role of hidden Markov models (HMMs) during minimum mean-square error (MMSE) spectral reconstruction. We develop novel HMM-based reconstruction algorithms which exploit intra-...

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Veröffentlicht in:	IEEE transactions on audio, speech, and language processing speech, and language processing, 2010-08, Vol.18 (6), p.1612-1623
Hauptverfasser:	Borgström, Bengt J, Alwan, Abeer
Format:	Artikel
Sprache:	eng
Schlagworte:	Applied sciences Coding, codes Computational efficiency Decoding Detection, estimation, filtering, equalization, prediction Estimating techniques Exact sciences and technology Hidden Markov models Hidden Markov models (HMMs) Information, signal and communications theory mask estimation Methods missing features (MFs) Noise noise robust speech recognition Noise robustness Reconstruction algorithms Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise spectral reconstruction Speech coding Speech processing Speech recognition Statistics Steady-state Stochastic resonance Studies Telecommunications and information theory
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