rVAD: An Unsupervised Segment-Based Robust Voice Activity Detection Method
Table 11: TD-SV performance of different VAD methods under noisy test environments on RedDots part-01 (male). Numbers in parenthesis show numbers of test utterances (out of total 3854 unique utterances) that do not yield any speech frames by the respective VAD methods.
['Noise type', 'SNR (dB)', '% Average EER [TW, IC, IW] across VAD methods no', '% Average EER [TW, IC, IW] across VAD methods Kaldi', '% Average EER [TW, IC, IW] across VAD methods Sohn', '% Average EER [TW, IC, IW] across VAD methods VQ', '% Average EER [TW, IC, IW] across VAD methods rVAD', '% Average EER [TW, IC, IW] across VAD methods rVAD', '% Average EER [TW, IC, IW] across VAD methods rVAD', '% Average EER [TW, IC, IW] across VAD methods rVAD-fast', '% Average EER [TW, IC, IW] across VAD methods rVAD-fast', '% Average EER [TW, IC, IW] across VAD methods rVAD-fast']
We further evaluate the performance of VAD methods for TD-SV under mismatched conditions, where noisy test utterances are scored against the speaker models trained under clean condition (office environment). In order to cover different real-world scenarios, various types of noise are artificially added to the test data with various SNR values. It is observed that TD-SV performances are significantly degraded under noisy conditions as also expected. rVAD achieves mostly lower EER values than those of Kaldi and Sohn et al. and No-VAD (i.e., without using VAD) over different noise types and SNR values, but performs slightly worse than VQVAD that is specially designed for SV. Sohn et al. VAD provides decent improvement as well. Kaldi Energy VAD degrades the performance compared with No-VAD, as in the case of clean condition. rVAD-fast gives comparable performance to that of rVAD under noise type of babble, market and car, but it does not work under white noise as the spectral flatness measure is severely affected by white noise. Therefore, the threshold value of θsft=0.5 does not output any speech frames with pitch (and thus no speech frames) for most of noisy test trails in SV and in this experiment, we consider these trails without speech frames as an SV error or misclassification. When calculating EER, genuine/true trials without speech frames are directly rejected (false rejection) by assigning the lowest score value available in the non-target trials to these genuine trials and vice-versa, namely non-target trails without speech frames are directly accepted (false alarm) by assigning the highest score value available in the genuine trials to these non-target trials.