Sciweavers

Share
TASLP
2010

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

9 years 4 months ago
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-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
Added 21 May 2011
Updated 21 May 2011
Type Journal
Year 2010
Where TASLP
Authors Bengt J. Borgstrom, Abeer Alwan
Comments (0)
books