Towards Robust Real-time Audio-Visual Speech Enhancement

• URL: http://arxiv.org/abs/2112.09060v1
• Date: Thu, 16 Dec 2021 17:54:45 GMT
• Title: Towards Robust Real-time Audio-Visual Speech Enhancement
• Authors: Mandar Gogate, Kia Dashtipour, Amir Hussain
• Abstract summary: We present a novel framework for low latency speaker-independent AV SE. In particular, a generative adversarial networks (GAN) is proposed to address the practical issue of visual imperfections in AV SE. We propose a deep neural network based real-time AV SE model that takes into account the cleaned visual speech output from GAN to deliver more robust SE.
• Score: 8.183895606832623
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The human brain contextually exploits heterogeneous sensory information to efficiently perform cognitive tasks including vision and hearing. For example, during the cocktail party situation, the human auditory cortex contextually integrates audio-visual (AV) cues in order to better perceive speech. Recent studies have shown that AV speech enhancement (SE) models can significantly improve speech quality and intelligibility in very low signal to noise ratio (SNR) environments as compared to audio-only SE models. However, despite significant research in the area of AV SE, development of real-time processing models with low latency remains a formidable technical challenge. In this paper, we present a novel framework for low latency speaker-independent AV SE that can generalise on a range of visual and acoustic noises. In particular, a generative adversarial networks (GAN) is proposed to address the practical issue of visual imperfections in AV SE. In addition, we propose a deep neural network based real-time AV SE model that takes into account the cleaned visual speech output from GAN to deliver more robust SE. The proposed framework is evaluated on synthetic and real noisy AV corpora using objective speech quality and intelligibility metrics and subjective listing tests. Comparative simulation results show that our real time AV SE framework outperforms state-of-the-art SE approaches, including recent DNN based SE models.

Related papers

• sVAD: A Robust, Low-Power, and Light-Weight Voice Activity Detection with Spiking Neural Networks [51.516451451719654]
  Spiking Neural Networks (SNNs) are known to be biologically plausible and power-efficient. This paper introduces a novel SNN-based Voice Activity Detection model, referred to as sVAD. It provides effective auditory feature representation through SincNet and 1D convolution, and improves noise robustness with attention mechanisms.
  arXiv  Detail & Related papers  (2024-03-09T02:55:44Z)
• AV2Wav: Diffusion-Based Re-synthesis from Continuous Self-supervised Features for Audio-Visual Speech Enhancement [18.193191170754744]
  We introduce AV2Wav, a re-synthesis-based audio-visual speech enhancement approach. We use continuous rather than discrete representations to retain prosody and speaker information. Our approach outperforms a masking-based baseline in terms of both automatic metrics and a human listening test.
  arXiv  Detail & Related papers  (2023-09-14T21:07:53Z)
• AdVerb: Visually Guided Audio Dereverberation [49.958724234969445]
  We present AdVerb, a novel audio-visual dereverberation framework. It uses visual cues in addition to the reverberant sound to estimate clean audio.
  arXiv  Detail & Related papers  (2023-08-23T18:20:59Z)
• AVFormer: Injecting Vision into Frozen Speech Models for Zero-Shot AV-ASR [79.21857972093332]
  We present AVFormer, a method for augmenting audio-only models with visual information, at the same time performing lightweight domain adaptation. We show that these can be trained on a small amount of weakly labelled video data with minimum additional training time and parameters. We also introduce a simple curriculum scheme during training which we show is crucial to enable the model to jointly process audio and visual information effectively.
  arXiv  Detail & Related papers  (2023-03-29T07:24:28Z)
• Prompt Tuning of Deep Neural Networks for Speaker-adaptive Visual Speech Recognition [66.94463981654216]
  We propose prompt tuning methods of Deep Neural Networks (DNNs) for speaker-adaptive Visual Speech Recognition (VSR) We finetune prompts on adaptation data of target speakers instead of modifying the pre-trained model parameters. The effectiveness of the proposed method is evaluated on both word- and sentence-level VSR databases.
  arXiv  Detail & Related papers  (2023-02-16T06:01:31Z)
• Self-Supervised Learning for Speech Enhancement through Synthesis [5.924928860260821]
  We propose a denoising vocoder (DeVo) approach, where a vocoder accepts noisy representations and learns to directly synthesize clean speech. We demonstrate a causal version capable of running on streaming audio with 10ms latency and minimal performance degradation.
  arXiv  Detail & Related papers  (2022-11-04T16:06:56Z)
• Self-supervised models of audio effectively explain human cortical responses to speech [71.57870452667369]
  We capitalize on the progress of self-supervised speech representation learning to create new state-of-the-art models of the human auditory system. We show that these results show that self-supervised models effectively capture the hierarchy of information relevant to different stages of speech processing in human cortex.
  arXiv  Detail & Related papers  (2022-05-27T22:04:02Z)
• Towards Intelligibility-Oriented Audio-Visual Speech Enhancement [8.19144665585397]
  We present a fully convolutional AV SE model that uses a modified short-time objective intelligibility (STOI) metric as a training cost function. Our proposed I-O AV SE framework outperforms audio-only (AO) and AV models trained with conventional distance-based loss functions.
  arXiv  Detail & Related papers  (2021-11-18T11:47:37Z)
• Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features [30.57631206882462]
  The MOSA-Net is designed to estimate speech quality, intelligibility, and distortion assessment scores based on a test speech signal as input. We show that the MOSA-Net can precisely predict perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI), and speech distortion index (BLS) scores when tested on both noisy and enhanced speech utterances.
  arXiv  Detail & Related papers  (2021-11-03T17:30:43Z)
• How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition [10.74796391075403]
  This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. We find that AV Align learns to align acoustic and visual representations of speech at the frame level on TCD-TIMIT in a generally monotonic pattern. We propose a regularisation method which involves predicting lip-related Action Units from visual representations.
  arXiv  Detail & Related papers  (2020-04-17T13:59:19Z)
• Deep Speaker Embeddings for Far-Field Speaker Recognition on Short Utterances [53.063441357826484]
  Speaker recognition systems based on deep speaker embeddings have achieved significant performance in controlled conditions. Speaker verification on short utterances in uncontrolled noisy environment conditions is one of the most challenging and highly demanded tasks. This paper presents approaches aimed to achieve two goals: a) improve the quality of far-field speaker verification systems in the presence of environmental noise, reverberation and b) reduce the system qualitydegradation for short utterances.
  arXiv  Detail & Related papers  (2020-02-14T13:34:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.