Recognition Of Reverberant Speech Using Frequency Domain Linear Prediction

Performance of a typical automatic speech recognition (ASR) system severely degrades when it encounters speech from reverberant environments. Part of the reason for this degradation is the feature extraction techniques that use analysis windows which are much shorter than typical room impulse responses. We present a feature extraction technique based on modeling temporal envelopes of the speech signal in narrow sub-bands using Frequency Domain Linear Prediction (FDLP). FDLP provides an all-pole approximation of the Hilbert envelope of the signal obtained by linear prediction on cosine transform of the signal. ASR experiments on speech data degraded with a number of room impulse responses (with varying degrees of distortion) show significant performance improvements for the proposed FDLP features when compared to other robust feature extraction techniques (average relative reduction of $24 \%$ in word error rate). Similar improvements are also obtained for far-field data which contain natural reverberation in background noise. These results are achieved without any noticeable degradation in performance for clean speech

Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments

We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, and represents the relative amount of diffuse noise in each time and frequency bin. It is shown that using the diffuseness feature as an additional input to a DNN-based acoustic model leads to a reduced word error rate for the REVERB challenge corpus, both compared to logmelspec features extracted from noisy signals, and features enhanced by spectral subtraction.Comment: accepted for ICASSP201