This paper presents a system aiming at joint dereverberation and noise reduction by applying a combination of a beamformer with a single-channel spectral enhancement scheme. First, a minimum variance distortionless response beamformer with an online estimated noise coherence matrix is used to suppress noise and reverberation. The output of this beamformer is then processed by a single-channel spectral enhancement scheme, based on statistical room acoustics, minimum statistics, and temporal cepstrum smoothing, to suppress residual noise and reverberation. The evaluation is conducted using the REVERB challenge corpus, designed to evaluate speech enhancement algorithms in the presence of both reverberation and noise. The proposed system is evaluated using instrumental speech quality measures, the performance of an automatic speech recognition system, and a subjective evaluation of the speech quality based on a MUSHRA test. The performance achieved by beamforming, single-channel spectral enhancement, and their combination are compared, and experimental results show that the proposed system is effective in suppressing both reverberation and noise while improving the speech quality. The achieved improvements are particularly significant in conditions with high reverberation times
The paper describes an automatic speech recognition (ASR) system for the 3rd CHiME challenge that addresses noisy acoustic scenes within public environments. The proposed system includes a multi-channel speech enhancement front-end including a microphone channel failure detection method that is based on cross-comparing the modulation spectra of speech to detect erroneous microphone recordings. The main focus of the submission is the investigation of the amplitude modulation filter bank (AMFB) as a method to extract long-term acoustic cues prior to a Gaussian mixture model (GMM) or deep neural network (DNN) based ASR classifier. It is shown that AMFB features outperform the commonly used frame splicing technique of filter bank features even on a performance optimized ASR challenge system. I.e., temporal analysis of speech by hand-crafted and auditory motivated AMFBs is shown to be more robust compared to a data-driven method based on extracting temporal dynamics with a DNN
In many speech communication applications, robust localization and tracking of multiple speakers in noisy and reverberant environments are of major importance. Several algorithms to tackle this problem have been proposed in the last decades. In this paper, we propose several extensions to a recently presented joint direction of arrival (DOA) and pitch estimation method, increasing its robustness in multi-speaker scenarios, noise, and reverberation. First, a spectral comb filter is added to the original algorithm to better cope with concurrent speakers. Second, the well-known generalized cross-correlation with phase transform (GCC-PHAT) is used as an additional weighting function to improve the DOA estimation accuracy in terms of correct hits. Third, using multiple microphone pairs, the multi-channel cross-correlation approach is incorporated to improve the robustness against noise and reverberation. In order to improve tracking for moving and even intersecting speakers, a particle filter is used. Experiments with real-world recordings in realistic acoustic conditions show that the proposed extensions increase the DOA hit rate by about 33% compared to the original algorithm for two step-wise moving sources at a signal-to-noise ratio (SNR) of 15 dB and a reverberation time RT 60 of 560 ms.
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