Visual Speech Enhancement

When video is shot in noisy environment, the voice of a speaker seen in the video can be enhanced using the visible mouth movements, reducing background noise. While most existing methods use audio-only inputs, improved performance is obtained with our visual speech enhancement, based on an audio-visual neural network. We include in the training data videos to which we added the voice of the target speaker as background noise. Since the audio input is not sufficient to separate the voice of a speaker from his own voice, the trained model better exploits the visual input and generalizes well to different noise types. The proposed model outperforms prior audio visual methods on two public lipreading datasets. It is also the first to be demonstrated on a dataset not designed for lipreading, such as the weekly addresses of Barack Obama.Comment: Accepted to Interspeech 2018. Supplementary video: https://www.youtube.com/watch?v=nyYarDGpcY

Light Gated Recurrent Units for Speech Recognition

A field that has directly benefited from the recent advances in deep learning is Automatic Speech Recognition (ASR). Despite the great achievements of the past decades, however, a natural and robust human-machine speech interaction still appears to be out of reach, especially in challenging environments characterized by significant noise and reverberation. To improve robustness, modern speech recognizers often employ acoustic models based on Recurrent Neural Networks (RNNs), that are naturally able to exploit large time contexts and long-term speech modulations. It is thus of great interest to continue the study of proper techniques for improving the effectiveness of RNNs in processing speech signals. In this paper, we revise one of the most popular RNN models, namely Gated Recurrent Units (GRUs), and propose a simplified architecture that turned out to be very effective for ASR. The contribution of this work is two-fold: First, we analyze the role played by the reset gate, showing that a significant redundancy with the update gate occurs. As a result, we propose to remove the former from the GRU design, leading to a more efficient and compact single-gate model. Second, we propose to replace hyperbolic tangent with ReLU activations. This variation couples well with batch normalization and could help the model learn long-term dependencies without numerical issues. Results show that the proposed architecture, called Light GRU (Li-GRU), not only reduces the per-epoch training time by more than 30% over a standard GRU, but also consistently improves the recognition accuracy across different tasks, input features, noisy conditions, as well as across different ASR paradigms, ranging from standard DNN-HMM speech recognizers to end-to-end CTC models.Comment: Copyright 2018 IEE

Deep learning for speech enhancement : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany, New Zealand

Speech enhancement, aiming at improving the intelligibility and overall perceptual quality of a contaminated speech signal, is an effective way to improve speech communications. In this thesis, we propose three novel deep learning methods to improve speech enhancement performance. Firstly, we propose an adversarial latent representation learning for latent space exploration of generative adversarial network based speech enhancement. Based on adversarial feature learning, this method employs an extra encoder to learn an inverse mapping from the generated data distribution to the latent space. The encoder establishes an inner connection with the generator and contributes to latent information learning. Secondly, we propose an adversarial multi-task learning with inverse mappings method for effective speech representation. This speech enhancement method focuses on enhancing the generator's capability of speech information capture and representation learning. To implement this method, two extra networks are developed to learn the inverse mappings from the generated distribution to the input data domains. Thirdly, we propose a self-supervised learning based phone-fortified method to improve specific speech characteristics learning for speech enhancement. This method explicitly imports phonetic characteristics into a deep complex convolutional network via a contrastive predictive coding model pre-trained with self-supervised learning. The experimental results demonstrate that the proposed methods outperform previous speech enhancement methods and achieve state-of-the-art performance in terms of speech intelligibility and overall perceptual quality