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Improved Speech Presence Probabilities Using HMM-Based Inference, with Applications to
 

Summary: 1
Improved Speech Presence Probabilities Using
HMM-Based Inference, with Applications to
Speech Enhancement and ASR
Bengt J. Borgstršom, Student Member, IEEE, and Abeer Alwan, IEEE Fellow
Abstract-- This paper presents a technique for determining
improved speech presence probabilities (SPPs), by exploiting the
temporal correlation present in spectral speech data. Based on
a set of traditional SPPs, we estimate the underlying speech
presence probability via statistical inference. Traditional SPPs are
assumed to be observations of channel-specific two-state Markov
models. Corresponding steady-state and transitional statistics are
set to capture the well-known temporal correlation of spectral
speech data, and observation statistics are modeled based on
the effect of additive acoustic noise on resulting SPPs. Once
underlying models have been parameterized, improved speech
presence probabilities can be estimated via traditional inference
techniques, such as the forward or forward-backward algorithms.
The 2-state configuration of underlying signal models enables
low complexity HMM-based processing, only slightly increasing

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences