11th Annual Conference of the International Speech Communication Association

Makuhari, Chiba, Japan
September 26-30. 2010

Efficient HMM-Based Estimation of Missing Features, with Applications to Packet Loss Concealment

Bengt J. Borgström, Per H. Borgström, Abeer Alwan

University of California at Los Angeles, USA

In this paper, we present efficient HMM-based techniques for estimating missing features. By assuming speech features to be observations of hidden Markov processes, we derive a minimum mean-square error (MMSE) solution. We increase the computational efficiency of HMM-based methods by downsampling underlying Markov models, and by enforcing symmetry in transitional probability matrices. When applied to features generally utilized in parametric speech coding, namely line spectral frequencies (LSFs), the proposed methods provide significant improvement over the baseline repetition scheme, in terms of weighted spectral distortion and peak SNR.

Full Paper

Bibliographic reference.  Borgström, Bengt J. / Borgström, Per H. / Alwan, Abeer (2010): "Efficient HMM-based estimation of missing features, with applications to packet loss concealment", In INTERSPEECH-2010, 2394-2397.