Deep Learning Based Speech Enhancement and Its Application to Speech Recognition

Speech enhancement is the task that aims to improve the quality and the intelligibility of a speech signal that is degraded by ambient noise and room reverberation. Speech enhancement algorithms are used extensively in many audio- and communication systems, including mobile handsets, speech recognition, speaker verification systems and hearing aids. Recently, deep learning has achieved great success in many applications, such as computer vision, nature language processing and speech recognition. Speech enhancement methods have been introduced that use deep-learning techniques, as these techniques are capable of learning complex hierarchical functions using large-scale training data. This dissertation investigates the deep learning based speech enhancement and its application to robust Automatic Speech Recognition (ASR). We start our work by exploring generative adversarial network (GAN) based speech enhancement. We explore the techniques to extract information about the noise to aid in the reconstruction of the speech signals. The proposed framework, referred to as ForkGAN, is a novel general adversarial learning-based framework that combines deep-learning with conventional noise reduction techniques. We further extend ForkGAN to M-ForkGAN, which integrates feature mapping and mask learning into a unified framework using ForkGAN. Another variant of ForkGAN, named S-ForkGAN, operates on spectral-domain features, which could directly apply to ASR. Systematic evaluations demonstrate the effectiveness of the proposed approaches. Then, we propose a novel multi-stage learning speech enhancement system. Each stage comprises a self-attention (SA) block followed by stacks of temporal convolutional network (TCN) blocks with doubling dilation factors. Each stage generates a prediction that is refined in a subsequent stage. A fusion block is inserted at the input of later stages to re-inject original information. Moreover, we design several multi-scale architectures with perceptual loss. Experiments show that our proposed architectures can achieve the state of the art performance on several public datasets. Recently, modeling to learn the acoustic noisy-clean speech mapping has been enhanced by including auxiliary information such as visual cues, phonetic and linguistic information, and speaker information. We propose a novel speaker-aware speech enhancement (SASE) method that extracts speaker information from a clean reference using long short-term memory (LSTM) layers, and then uses a convolutional recurrent neural network (CRN) to embed the extracted speaker information. The SASE framework is extended with a self-attention mechanism. It is shown that a few seconds of clean reference speech is sufficient, and that the proposed SASE method performs well for a wide range of scenarios. Even though speech enhancement methods that are based on deep learning have demonstrated state-of-the-art performance when compared with conventional methodologies, current deep learning approaches heavily rely on supervised learning, which requires a large number of noisy- and clean-speech sample pairs for training. This is generally not practical in a realistic environment. One cannot simultaneously obtain both noisy and clean speech samples. Thus, most speech enhancement approaches are trained with simulated speech and clean targets. In addition, it would be hard to collect large-scale dataset for the low-resource languages. We propose a novel noise-to-noise speech enhancement (N2N-SE) method that addresses the parallel noisy-clean training data issue, we leverage signal reconstruction techniques by only using corrupted speech. The proposed N2N-SE framework includes a noise conversion module that is an auto-encoder that learns to mix noise with speech, and a speech enhancement module, that learns to reconstruct corrupted speech signals. In addition to additive noise, speech is also affected by reverberation, which is caused by the attenuated and delayed reflections of sound waves. These distortions, particularly when combined, can severely degrade speech intelligibility for human listeners and impact applications, e.g., automatic speech recognition (ASR) and speaker recognition. Thus, effective speech denoising and dereverberation will benefit both speech processing applications and human listeners. We investigate the deep-learning based approaches for both speech dereverberation and speech denoising using the cascade Conformer architecture. The experimental results show that the proposed cascade Conformer can be effective to suppress the noise and reverberation

Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring

Jose Manuel Milla, Sergio Luis Toral, Manuel Vargas and Federico Barrero (2010). Computer Vision Techniques for Background Modeling in Urban Traffic Monitoring, Urban Transport and Hybrid Vehicles, Seref Soylu (Ed.), ISBN: 978-953-307-100-8, InTech, DOI: 10.5772/10179. Available from: http://www.intechopen.com/books/urban-transport-and-hybrid-vehicles/computer-vision-techniques-for-background-modeling-in-urban-traffic-monitoringIn this chapter, several background modelling techniques have been described, analyzed and tested. In particular, different algorithms based on sigma-delta filter have been considered due to their suitability for embedded systems, where computational limitations affect a real-time implementation. A qualitative and a quantitative comparison have been performed among the different algorithms. Obtained results show that the sigma-delta algorithm with confidence measurement exhibits the best performance in terms of adaptation to particular specificities of urban traffic scenes and in terms of computational requirements. A prototype based on an ARM processor has been implemented to test the different versions of the sigma-delta algorithm and to illustrate several applications related to vehicle traffic monitoring and implementation details

A Survey on Non-Autoregressive Generation for Neural Machine Translation and Beyond

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation (NMT) to speed up inference, has attracted much attention in both machine learning and natural language processing communities. While NAR generation can significantly accelerate inference speed for machine translation, the speedup comes at the cost of sacrificed translation accuracy compared to its counterpart, auto-regressive (AR) generation. In recent years, many new models and algorithms have been designed/proposed to bridge the accuracy gap between NAR generation and AR generation. In this paper, we conduct a systematic survey with comparisons and discussions of various non-autoregressive translation (NAT) models from different aspects. Specifically, we categorize the efforts of NAT into several groups, including data manipulation, modeling methods, training criterion, decoding algorithms, and the benefit from pre-trained models. Furthermore, we briefly review other applications of NAR models beyond machine translation, such as dialogue generation, text summarization, grammar error correction, semantic parsing, speech synthesis, and automatic speech recognition. In addition, we also discuss potential directions for future exploration, including releasing the dependency of KD, dynamic length prediction, pre-training for NAR, and wider applications, etc. We hope this survey can help researchers capture the latest progress in NAR generation, inspire the design of advanced NAR models and algorithms, and enable industry practitioners to choose appropriate solutions for their applications. The web page of this survey is at \url{https://github.com/LitterBrother-Xiao/Overview-of-Non-autoregressive-Applications}.Comment: 25 pages, 11 figures, 4 table

Binaural scene analysis : localization, detection and recognition of speakers in complex acoustic scenes

The human auditory system has the striking ability to robustly localize and recognize a specific target source in complex acoustic environments while ignoring interfering sources. Surprisingly, this remarkable capability, which is referred to as auditory scene analysis, is achieved by only analyzing the waveforms reaching the two ears. Computers, however, are presently not able to compete with the performance achieved by the human auditory system, even in the restricted paradigm of confronting a computer algorithm based on binaural signals with a highly constrained version of auditory scene analysis, such as localizing a sound source in a reverberant environment or recognizing a speaker in the presence of interfering noise. In particular, the problem of focusing on an individual speech source in the presence of competing speakers, termed the cocktail party problem, has been proven to be extremely challenging for computer algorithms. The primary objective of this thesis is the development of a binaural scene analyzer that is able to jointly localize, detect and recognize multiple speech sources in the presence of reverberation and interfering noise. The processing of the proposed system is divided into three main stages: localization stage, detection of speech sources, and recognition of speaker identities. The only information that is assumed to be known a priori is the number of target speech sources that are present in the acoustic mixture. Furthermore, the aim of this work is to reduce the performance gap between humans and machines by improving the performance of the individual building blocks of the binaural scene analyzer. First, a binaural front-end inspired by auditory processing is designed to robustly determine the azimuth of multiple, simultaneously active sound sources in the presence of reverberation. The localization model builds on the supervised learning of azimuthdependent binaural cues, namely interaural time and level differences. Multi-conditional training is performed to incorporate the uncertainty of these binaural cues resulting from reverberation and the presence of competing sound sources. Second, a speech detection module that exploits the distinct spectral characteristics of speech and noise signals is developed to automatically select azimuthal positions that are likely to correspond to speech sources. Due to the established link between the localization stage and the recognition stage, which is realized by the speech detection module, the proposed binaural scene analyzer is able to selectively focus on a predefined number of speech sources that are positioned at unknown spatial locations, while ignoring interfering noise sources emerging from other spatial directions. Third, the speaker identities of all detected speech sources are recognized in the final stage of the model. To reduce the impact of environmental noise on the speaker recognition performance, a missing data classifier is combined with the adaptation of speaker models using a universal background model. This combination is particularly beneficial in nonstationary background noise

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem