Audio-Visual Efficient Conformer for Robust Speech Recognition (2301.01456v1)

Abstract: End-to-end Automatic Speech Recognition (ASR) systems based on neural networks have seen large improvements in recent years. The availability of large scale hand-labeled datasets and sufficient computing resources made it possible to train powerful deep neural networks, reaching very low Word Error Rate (WER) on academic benchmarks. However, despite impressive performance on clean audio samples, a drop of performance is often observed on noisy speech. In this work, we propose to improve the noise robustness of the recently proposed Efficient Conformer Connectionist Temporal Classification (CTC)-based architecture by processing both audio and visual modalities. We improve previous lip reading methods using an Efficient Conformer back-end on top of a ResNet-18 visual front-end and by adding intermediate CTC losses between blocks. We condition intermediate block features on early predictions using Inter CTC residual modules to relax the conditional independence assumption of CTC-based models. We also replace the Efficient Conformer grouped attention by a more efficient and simpler attention mechanism that we call patch attention. We experiment with publicly available Lip Reading Sentences 2 (LRS2) and Lip Reading Sentences 3 (LRS3) datasets. Our experiments show that using audio and visual modalities allows to better recognize speech in the presence of environmental noise and significantly accelerate training, reaching lower WER with 4 times less training steps. Our Audio-Visual Efficient Conformer (AVEC) model achieves state-of-the-art performance, reaching WER of 2.3% and 1.8% on LRS2 and LRS3 test sets. Code and pretrained models are available at https://github.com/burchim/AVEC.

Audio-Visual Efficient Conformer for Robust Speech Recognition: An Analysis

The paper "Audio-Visual Efficient Conformer for Robust Speech Recognition" presents an innovative approach to enhancing Automatic Speech Recognition (ASR) systems through the integration of audio and visual data processing. The proposed method tackles the notable challenge of handling noisy speech inputs that traditionally degrade the performance of ASR systems which predominantly rely on audio signals alone.

A thorough evaluation was conducted using notable datasets such as LRS2 and LRS3, demonstrating that the Audio-Visual Efficient Conformer (AVEC) model achieves superior noise robustness compared to certain state-of-the-art methods. This paper makes several technical contributions worth noting:

1. Audiovisual Integration: The paper effectively combines audio signals with visual data of lip movements. This dual-modal integration aids in filtering out noise, subsequently enhancing the robustness and performance of the ASR system in noise-rich environments.
2. Efficient Conformer Architecture Adaptations: An Efficient Conformer back-end, complemented with ResNet-18 for the visual front-end, was employed. Notably, an innovative patch attention mechanism supersedes traditional grouped attention, optimizing computational resources without sacrificing performance quality. This approach departs from the complex structures inherent in other architectures by focusing on streamlined operations.
3. Intermediate CTC Loss: The introduction of intermediate Connectionist Temporal Classification (CTC) losses between Conformer blocks provides a mechanism to relax the conditional independence assumptions of CTC models. This strategy not only improves recognition performances but also facilitates a convergence on reduced Word Error Rates (WER) for both audio-only and audio-visual models.
4. Data Processing and Augmentation: The preprocessing methods include applying Fourier transforms and leveraging face detection and normalization techniques, essential for standardizing both audio and video data inputs. Combined with Spec-Augment and other augmentation strategies, these processes further enhance the robustness against variability in input data.

The paper's results were compelling, with the AVEC model achieving a WER of 2.3% on the LRS2 test set and 1.8% on LRS3, marking it as one of the leading models utilizing publicly available datasets for AVSR tasks. Significantly, the AVEC model achieved these results with computational efficiency, necessitating fewer training epochs.

Regarding implications, the paper's findings suggest a broadened scope for ASR applications in real-world scenarios where ambient noise is a concern. The dual-processing of auditory and visual modalities opens pathways for devices operating in noisy environments to maintain high recognition accuracy. This suggests potential applications not only in consumer electronics but also in environments such as automotive voice control systems and assistive technologies for the hearing impaired.

This research presents a noteworthy step forward in the development of more reliable, efficient, and scalable ASR systems. Future research directions could explore improving the integration of visual processing to address situations where speakers or their lips might not be visible or partially obscured. Moreover, further exploration into cross-modal distillation strategies may untether robustness and accuracy in broader multimedia contexts. Ultimately, the paper lays an important groundwork for advancing AVSR technologies into more pragmatic and generalized use cases.