Block-Online Multi-Channel Speech Enhancement Using DNN-Supported Relative Transfer Function Estimates

This work addresses the problem of block-online processing for multi-channel speech enhancement. Such processing is vital in scenarios with moving speakers and/or when very short utterances are processed, e.g., in voice assistant scenarios. We consider several variants of a system that performs beamforming supported by DNN-based voice activity detection (VAD) followed by post-filtering. The speaker is targeted through estimating relative transfer functions between microphones. Each block of the input signals is processed independently in order to make the method applicable in highly dynamic environments. Owing to the short length of the processed block, the statistics required by the beamformer are estimated less precisely. The influence of this inaccuracy is studied and compared to the processing regime when recordings are treated as one block (batch processing). The experimental evaluation of the proposed method is performed on large datasets of CHiME-4 and on another dataset featuring moving target speaker. The experiments are evaluated in terms of objective and perceptual criteria (such as signal-to-interference ratio (SIR) or perceptual evaluation of speech quality (PESQ), respectively). Moreover, word error rate (WER) achieved by a baseline automatic speech recognition system is evaluated, for which the enhancement method serves as a front-end solution. The results indicate that the proposed method is robust with respect to short length of the processed block. Significant improvements in terms of the criteria and WER are observed even for the block length of 250 ms.Comment: 10 pages, 8 figures, 4 tables. Modified version of the article accepted for publication in IET Signal Processing journal. Original results unchanged, additional experiments presented, refined discussion and conclusion

A voice activity detection algorithm with sub-band detection based on time-frequency characteristics of mandarin

Voice activity detection algorithms are widely used in the areas of voice compression, speech synthesis, speech recognition, speech enhancement, and etc. In this paper, an efficient voice activity detection algorithm with sub-band detection based on time-frequency characteristics of mandarin is proposed. The proposed sub-band detection consists of two parts: crosswise detection and lengthwise detection. Energy detection and pitch detection are in the range of considerations. For a better performance, double-threshold criterion is used to reduce the misjudgment rate of the detection. Performance evaluation is based on six noise environments with different SNRs. Experiment results indicate that the proposed algorithm can detect the area of voice effectively in non-stationary environment and low SNR environment and has the potential to progress

Influence of transition cost in the segmentation stage of speaker diarization

In any speaker diarization system there is a segmentation phase and a clustering phase. Our system uses them in a single step in which segmentation and clustering are used iteratively until certain condition is met. In this paper we propose an improvement of the segmentation method that cancels a penalization that had been applied in previous works to any transition between speakers. We also study the performance when transitions between speakers are favoured instead of penalized. This last option achieves better results both for the development set (21.65 % relative speaker error improvementSER) and for the test set (4.60% relative speaker error improvement

Voice activity detection based on density ratio estimation and system combination

Abstract-We propose a robust voice activity detection (VAD) based on density ratio estimation. In highly noisy environments, the likelihood ratio test (LRT) is effective. Conventional LRT estimates both speech and noise models, calculates the likelihood of each model, and uses ratios of such likelihood to detect speech. However, in LRT, the likelihood ratio of speech and noise models is required, whereas likelihood of individual models is not necessarily required. The framework of the density ratio estimation models likelihood ratio functions by a kernel and directly generates a likelihood ratio. Applying density ratio estimation to VAD requires that feature selection and noise adaptation must be considered. This is because the density ratio estimation constrains the shape of the likelihood ratio functions and speech is dynamic. This paper addresses these problems. To improve accuracy, the proposed method is combined with conventional LRT. Experimental results using CENSREC-1-C show that the proposed method is more effective than conventional methods, especially in non-stationary noisy environments