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

12 years 11 months ago

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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-channel (across-time) correlation and/or inter-channel (across-frequency) correlation. For the sake of computational efficiency, this paper utilizes approximations to HMM-based decoding methods by developing models constructed from lower resolution quantizers. State configurations for lower resolution models are obtained through a tree-structured mapping of quantizer centroids, and model parameters are adapted accordingly. HMM downsampling avoids expensive re-training of models, and eliminates unnecessary memory requirements. Explicit general formulae are presented for the adaptation of steady-state and transitional statistics. Adaptation of observation statistics are derived from stochastic...

Bengt J. Borgstrom, Abeer Alwan

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