Learn More
This work presents an automatic speech recognition system which uses a missing data approach to compensate for environmental noise. The missing, noise-corrupted components are identified using binaural features or a support vector machine (SVM) classifier. To perform speech recognition using the partially observed data, the missing components are(More)
The problem of reverberation in speech recognition is addressed in this study by extending a noise-robust feature enhancement method based on non-negative matrix factor-ization. The signal model of the observation as a linear combination of sample spectrograms is augmented by a mel-spectral feature domain convolution to account for the effects of room(More)
Following earlier work, we modify linear predictive (LP) speech analysis by including temporal weighting of the squared prediction error in the model optimization. In order to focus this so called weighted LP model on the least noisy signal regions in the presence of stationary additive noise, we use short-time signal energy as the weighting function. We(More)
This work studies the use of observation uncertainty measures for improving the speech recognition performance of an exemplar-based source separation based front end. To generate the observation uncertainty estimates for the enhanced features, we propose the use of heuristic methods based on the sparse representation of the noisy signal in the(More)
We present a method of improving automatic speech recognition performance under noisy conditions by using a source separation approach to extract the underlying clean speech signal. The feature enhancement processing is complemented with heuristic estimates of the uncertainty of the source separation, that are used to further assist the recognition. The(More)
In this work, we present a missing feature reconstruction based automatic speech recognition (ASR) system in which masks are estimated by binary classification of features generated by Gaussian-Bernoulli restricted Boltzmann machines (GRBMs). The system is evaluated on Track 1 of the 2nd CHiME challenge data. Overall, the best performance is achieved when(More)
This paper describes a novel two-stage dereverberation feature enhancement method for noise-robust automatic speech recognition. In the first stage, an estimate of the dereverberated speech is generated by matching the distribution of the observed reverberant speech to that of clean speech, in a decorrelated transformation domain that has a long temporal(More)