Discriminative Multi-modality Speech Recognition
- URL: http://arxiv.org/abs/2005.05592v2
- Date: Wed, 13 May 2020 07:55:21 GMT
- Title: Discriminative Multi-modality Speech Recognition
- Authors: Bo Xu, Cheng Lu, Yandong Guo and Jacob Wang
- Abstract summary: Vision is often used as a complementary modality for audio speech recognition (ASR)
In this paper, we propose a two-stage speech recognition model.
In the first stage, the target voice is separated from background noises with help from the corresponding visual information of lip movements, making the model 'listen' clearly.
At the second stage, the audio modality combines visual modality again to better understand the speech by a MSR sub-network, further improving the recognition rate.
- Score: 17.296404414250553
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Vision is often used as a complementary modality for audio speech recognition
(ASR), especially in the noisy environment where performance of solo audio
modality significantly deteriorates. After combining visual modality, ASR is
upgraded to the multi-modality speech recognition (MSR). In this paper, we
propose a two-stage speech recognition model. In the first stage, the target
voice is separated from background noises with help from the corresponding
visual information of lip movements, making the model 'listen' clearly. At the
second stage, the audio modality combines visual modality again to better
understand the speech by a MSR sub-network, further improving the recognition
rate. There are some other key contributions: we introduce a pseudo-3D residual
convolution (P3D)-based visual front-end to extract more discriminative
features; we upgrade the temporal convolution block from 1D ResNet with the
temporal convolutional network (TCN), which is more suitable for the temporal
tasks; the MSR sub-network is built on the top of Element-wise-Attention Gated
Recurrent Unit (EleAtt-GRU), which is more effective than Transformer in long
sequences. We conducted extensive experiments on the LRS3-TED and the LRW
datasets. Our two-stage model (audio enhanced multi-modality speech
recognition, AE-MSR) consistently achieves the state-of-the-art performance by
a significant margin, which demonstrates the necessity and effectiveness of
AE-MSR.
Related papers
- VHASR: A Multimodal Speech Recognition System With Vision Hotwords [74.94430247036945]
VHASR is a multimodal speech recognition system that uses vision as hotwords to strengthen the model's speech recognition capability.
VHASR can effectively utilize key information in images to enhance the model's speech recognition ability.
arXiv Detail & Related papers (2024-10-01T16:06:02Z) - MLCA-AVSR: Multi-Layer Cross Attention Fusion based Audio-Visual Speech Recognition [62.89464258519723]
We propose a multi-layer cross-attention fusion based AVSR approach that promotes representation of each modality by fusing them at different levels of audio/visual encoders.
Our proposed approach surpasses the first-place system, establishing a new SOTA cpCER of 29.13% on this dataset.
arXiv Detail & Related papers (2024-01-07T08:59:32Z) - Improving Audio-Visual Speech Recognition by Lip-Subword Correlation
Based Visual Pre-training and Cross-Modal Fusion Encoder [58.523884148942166]
We propose two novel techniques to improve audio-visual speech recognition (AVSR) under a pre-training and fine-tuning training framework.
First, we explore the correlation between lip shapes and syllable-level subword units in Mandarin to establish good frame-level syllable boundaries from lip shapes.
Next, we propose an audio-guided cross-modal fusion encoder (CMFE) neural network to utilize main training parameters for multiple cross-modal attention layers.
arXiv Detail & Related papers (2023-08-14T08:19:24Z) - 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) - Leveraging Modality-specific Representations for Audio-visual Speech
Recognition via Reinforcement Learning [25.743503223389784]
We propose a reinforcement learning (RL) based framework called MSRL.
We customize a reward function directly related to task-specific metrics.
Experimental results on the LRS3 dataset show that the proposed method achieves state-of-the-art in both clean and various noisy conditions.
arXiv Detail & Related papers (2022-12-10T14:01:54Z) - Audio-visual multi-channel speech separation, dereverberation and
recognition [70.34433820322323]
This paper proposes an audio-visual multi-channel speech separation, dereverberation and recognition approach.
The advantage of the additional visual modality over using audio only is demonstrated on two neural dereverberation approaches.
Experiments conducted on the LRS2 dataset suggest that the proposed audio-visual multi-channel speech separation, dereverberation and recognition system outperforms the baseline.
arXiv Detail & Related papers (2022-04-05T04:16:03Z) - Audio-visual Multi-channel Recognition of Overlapped Speech [79.21950701506732]
This paper presents an audio-visual multi-channel overlapped speech recognition system featuring tightly integrated separation front-end and recognition back-end.
Experiments suggest that the proposed multi-channel AVSR system outperforms the baseline audio-only ASR system by up to 6.81% (26.83% relative) and 22.22% (56.87% relative) absolute word error rate (WER) reduction on overlapped speech constructed using either simulation or replaying of the lipreading sentence 2 dataset respectively.
arXiv Detail & Related papers (2020-05-18T10:31:19Z) - Multiresolution and Multimodal Speech Recognition with Transformers [22.995102995029576]
This paper presents an audio visual automatic speech recognition (AV-ASR) system using a Transformer-based architecture.
We focus on the scene context provided by the visual information, to ground the ASR.
Our results are comparable to state-of-the-art Listen, Attend and Spell-based architectures.
arXiv Detail & Related papers (2020-04-29T09:32:11Z)
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.