On Hidden Markov Model Maximum Negentropy Beamforming
نویسندگان
چکیده
In prior work, we developed a beamforming algorithm intended for automatic recognition of speech data captured with an array of distant microphones. In addition to enforcing a distortionless contraint in a desired direction, we adjusted the sensor weights so as to maximimize a negentropy criterion. Negentropy is a measure of how non-Gaussian the probability density function (pdf) of a random variable is. It is known that subband samples of speech are highly non-Gaussian, but become more Gaussian when corrupted with noise or reverberation. Here we extend our prior algorithm by using an auxiliary hidden Markov model to model the nonstationarity of speech during beamforming. In a set of far-field ASR experiments on data from the Multi-Channel Wall Street Journal Audio-Visual Corpus, we were able to reduce the word error rate from 14.6% to 13.6% by accounting for this non-stationarity.
منابع مشابه
Modelling the nonstationarity of speech in the maximum negentropy beamformer
State-of-the-art automatic speech recognition (ASR) systems can achieve very low word error rates (WERs) of below 5% on data recorded with headsets. However, in many situations such as ASR at meetings or in the car, far field microphones on the table, walls or devices such as laptops are preferable to microphones that have to be worn close to the user’s mouths. Unfortunately, the distance betwe...
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