608 research outputs found
Codebook-based Bayesian speech enhancement for nonstationary environments
In this paper, we propose a Bayesian minimum mean squared error approach for the joint estimation of the short-term predictor parameters of speech and noise, from the noisy observation. We use trained codebooks of speech and noise linear predictive coefficients to model the a priori information required by the Bayesian scheme. In contrast to current Bayesian estimation approaches that consider the excitation variances as part of the a priori information, in the proposed method they are computed online for each short-time segment, based on the observation at hand. Consequently, the method performs well in nonstationary noise conditions. The resulting estimates of the speech and noise spectra can be used in a Wiener filter or any state-of-the-art speech enhancement system. We develop both memoryless (using information from the current frame alone) and memory-based (using information from the current and previous frames) estimators. Estimation of functions of the short-term predictor parameters is also addressed, in particular one that leads to the minimum mean squared error estimate of the clean speech signal. Experiments indicate that the scheme proposed in this paper performs significantly better than competing method
A Bayesian Network View on Acoustic Model-Based Techniques for Robust Speech Recognition
This article provides a unifying Bayesian network view on various approaches
for acoustic model adaptation, missing feature, and uncertainty decoding that
are well-known in the literature of robust automatic speech recognition. The
representatives of these classes can often be deduced from a Bayesian network
that extends the conventional hidden Markov models used in speech recognition.
These extensions, in turn, can in many cases be motivated from an underlying
observation model that relates clean and distorted feature vectors. By
converting the observation models into a Bayesian network representation, we
formulate the corresponding compensation rules leading to a unified view on
known derivations as well as to new formulations for certain approaches. The
generic Bayesian perspective provided in this contribution thus highlights
structural differences and similarities between the analyzed approaches
Speech Separation Using Partially Asynchronous Microphone Arrays Without Resampling
We consider the problem of separating speech sources captured by multiple
spatially separated devices, each of which has multiple microphones and samples
its signals at a slightly different rate. Most asynchronous array processing
methods rely on sample rate offset estimation and resampling, but these offsets
can be difficult to estimate if the sources or microphones are moving. We
propose a source separation method that does not require offset estimation or
signal resampling. Instead, we divide the distributed array into several
synchronous subarrays. All arrays are used jointly to estimate the time-varying
signal statistics, and those statistics are used to design separate
time-varying spatial filters in each array. We demonstrate the method for
speech mixtures recorded on both stationary and moving microphone arrays.Comment: To appear at the International Workshop on Acoustic Signal
Enhancement (IWAENC 2018
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