Visual Voice Activity Detection (V-VAD) involves the detection of speech activity of a speaker using visual features. The V-VAD is useful in detecting the end point of an utterance under noisy acoustic conditions or for maintaining speaker privacy. In this paper, we propose a speaker independent, real-time solution for V-VAD. The focus is on real-time aspect and accuracy as such algorithms will play a key role in detecting end point especially while interacting with speech assistants. We propose two novel methods one using CNN and the other using 2D-DCT features. Unidirectional LSTMs are used in both the methods to make it online and learn temporal dependence. The methods are tested on two publicly available datasets. Additionally the methods are also tested on a locally collected dataset which further validates our hypothesis. Additionally it has been observed through experiments that both the approaches generalize to unseen speakers. It has been shown that our best approach gives substantial improvement over earlier methods done on the same dataset.

Visual speech recognition or lipreading suffers from high word error rate (WER) as lipreading is based solely on articulators that are visible to the camera. Recent works mitigated this problem using complex architectures of deep neural networks. Ivector based speaker adaptation is a well known technique in ASR systems used to reduce WER on unseen speakers. In this work, we explore speaker adaptation of lipreading models using latent identity vectors (visual i-vectors) obtained by factor analysis on visual features. In order to estimate the visual i-vectors, we employ two ways to collect sufficient statistics: first using GMM based universal background model (UBM) and second using RNN-HMM based UBM. The speaker-specific visual i-vector is given as an additional input to the hidden layers of the lipreading model during train and test phases. On GRID corpus, use of visual i-vectors results in 15% and 10% relative improvements over current state of the art lipreading architectures on unseen speakers using RNN-HMM and GMM based methods respectively. Furthermore, we explore the variation of WER with dimension of visual i-vectors, and with the amount of unseen speaker data required for visual i-vector estimation. We also report the results on Korean visual corpus that we created.

This paper demonstrates two novel methods to estimate the global SNR of speech signals. In both methods, Deep Neural Network-Hidden Markov Model (DNN-HMM) acoustic model used in speech recognition systems is leveraged for the additional task of SNR estimation. In the first method, the entropy of the DNN-HMM output is computed. Recent work on bayesian deep learning has shown that a DNN-HMM trained with dropout can be used to estimate model uncertainty by approximating it as a deep Gaussian process. In the second method, this approximation is used to obtain model uncertainty estimates. Noise specific regressors are used to predict the SNR from the entropy and model uncertainty. The DNN-HMM is trained on GRID corpus and tested on different noise profiles from the DEMAND noise database at SNR levels ranging from -10 dB to 30 dB.

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