End-to-end Audiovisual Speech Activity Detection with Bimodal Recurrent Neural Models

Speech activity detection (SAD) plays an important role in current speech processing systems, including automatic speech recognition (ASR). SAD is particularly difficult in environments with acoustic noise. A practical solution is to incorporate visual information, increasing the robustness of the SAD approach. An audiovisual system has the advantage of being robust to different speech modes (e.g., whisper speech) or background noise. Recent advances in audiovisual speech processing using deep learning have opened opportunities to capture in a principled way the temporal relationships between acoustic and visual features. This study explores this idea proposing a \emph{bimodal recurrent neural network} (BRNN) framework for SAD. The approach models the temporal dynamic of the sequential audiovisual data, improving the accuracy and robustness of the proposed SAD system. Instead of estimating hand-crafted features, the study investigates an end-to-end training approach, where acoustic and visual features are directly learned from the raw data during training. The experimental evaluation considers a large audiovisual corpus with over 60.8 hours of recordings, collected from 105 speakers. The results demonstrate that the proposed framework leads to absolute improvements up to 1.2% under practical scenarios over a VAD baseline using only audio implemented with deep neural network (DNN). The proposed approach achieves 92.7% F1-score when it is evaluated using the sensors from a portable tablet under noisy acoustic environment, which is only 1.0% lower than the performance obtained under ideal conditions (e.g., clean speech obtained with a high definition camera and a close-talking microphone).Comment: Submitted to Speech Communicatio

A study of lip movements during spontaneous dialog and its application to voice activity detection

International audienceThis paper presents a quantitative and comprehensive study of the lip movements of a given speaker in different speech/nonspeech contexts, with a particular focus on silences i.e., when no sound is produced by the speaker . The aim is to characterize the relationship between "lip activity" and "speech activity" and then to use visual speech information as a voice activity detector VAD . To this aim, an original audiovisual corpus was recorded with two speakers involved in a face-to-face spontaneous dialog, although being in separate rooms. Each speaker communicated with the other using a microphone, a camera, a screen, and headphones. This system was used to capture separate audio stimuli for each speaker and to synchronously monitor the speaker's lip movements. A comprehensive analysis was carried out on the lip shapes and lip movements in either silence or nonsilence i.e., speech+nonspeech audible events . A single visual parameter, defined to characterize the lip movements, was shown to be efficient for the detection of silence sections. This results in a visual VAD that can be used in any kind of environment noise, including intricate and highly nonstationary noises, e.g., multiple and/or moving noise sources or competing speech signals

Visual voice activity detection as a help for speech source separation from convolutive mixtures

International audienceAudio–visual speech source separation consists in mixing visual speech processing techniques (e.g., lip parameters tracking) with source separation methods to improve the extraction of a speech source of interest from a mixture of acoustic signals. In this paper, we present a new approach that combines visual information with separation methods based on the sparseness of speech: visual information is used as a voice activity detector (VAD) which is combined with a new geometric method of separation. The proposed audio–visual method is shown to be efficient to extract a real spontaneous speech utterance in the difficult case of convolutive mixtures even if the competing sources are highly non-stationary. Typical gains of 18–20 dB in signal to interference ratios are obtained for a wide range of (2 × 2) and (3 × 3) mixtures. Moreover, the overall process is computationally quite simpler than previously proposed audio–visual separation schemes

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploiting the bimodality of speech in the cocktail party problem

The cocktail party problem is one of following a conversation in a crowded room where there are many competing sound sources, such as the voices of other speakers or music. To address this problem using computers, digital signal processing solutions commonly use blind source separation (BSS) which aims to separate all the original sources (voices) from the mixture simultaneously. Traditionally, BSS methods have relied on information derived from the mixture of sources to separate the mixture into its constituent elements. However, the human auditory system is well adapted to handle the cocktail party scenario, using both auditory and visual information to follow (or hold) a conversation in a such an environment. This thesis focuses on using visual information of the speakers in a cocktail party like scenario to aid in improving the performance of BSS. There are several useful applications of such technology, for example: a pre-processing step for a speech recognition system, teleconferencing or security surveillance. The visual information used in this thesis is derived from the speaker's mouth region, as it is the most visible component of speech production. Initial research presented in this thesis considers a joint statistical model of audio and visual features, which is used to assist in control ling the convergence behaviour of a BSS algorithm. The results of using the statistical models are compared to using the raw audio information alone and it is shown that the inclusion of visual information greatly improves its convergence behaviour. Further research focuses on using the speaker's mouth region to identify periods of time when the speaker is silent through the development of a visual voice activity detector (V-VAD) (i.e. voice activity detection using visual information alone). This information can be used in many different ways to simplify the BSS process. To this end, two novel V-VADs were developed and tested within a BSS framework, which result in significantly improved intelligibility of the separated source associated with the V-VAD output. Thus the research presented in this thesis confirms the viability of using visual information to improve solutions to the cocktail party problem

Towards An Intelligent Fuzzy Based Multimodal Two Stage Speech Enhancement System

This thesis presents a novel two stage multimodal speech enhancement system, making use of both visual and audio information to filter speech, and explores the extension of this system with the use of fuzzy logic to demonstrate proof of concept for an envisaged autonomous, adaptive, and context aware multimodal system. The design of the proposed cognitively inspired framework is scalable, meaning that it is possible for the techniques used in individual parts of the system to be upgraded and there is scope for the initial framework presented here to be expanded. In the proposed system, the concept of single modality two stage filtering is extended to include the visual modality. Noisy speech information received by a microphone array is first pre-processed by visually derived Wiener filtering employing the novel use of the Gaussian Mixture Regression (GMR) technique, making use of associated visual speech information, extracted using a state of the art Semi Adaptive Appearance Models (SAAM) based lip tracking approach. This pre-processed speech is then enhanced further by audio only beamforming using a state of the art Transfer Function Generalised Sidelobe Canceller (TFGSC) approach. This results in a system which is designed to function in challenging noisy speech environments (using speech sentences with different speakers from the GRID corpus and a range of noise recordings), and both objective and subjective test results (employing the widely used Perceptual Evaluation of Speech Quality (PESQ) measure, a composite objective measure, and subjective listening tests), showing that this initial system is capable of delivering very encouraging results with regard to filtering speech mixtures in difficult reverberant speech environments. Some limitations of this initial framework are identified, and the extension of this multimodal system is explored, with the development of a fuzzy logic based framework and a proof of concept demonstration implemented. Results show that this proposed autonomous,adaptive, and context aware multimodal framework is capable of delivering very positive results in difficult noisy speech environments, with cognitively inspired use of audio and visual information, depending on environmental conditions. Finally some concluding remarks are made along with proposals for future work