Visual Speech Enhancement Without A Real Visual Stream (2012.10852v1)

Abstract: In this work, we re-think the task of speech enhancement in unconstrained real-world environments. Current state-of-the-art methods use only the audio stream and are limited in their performance in a wide range of real-world noises. Recent works using lip movements as additional cues improve the quality of generated speech over "audio-only" methods. But, these methods cannot be used for several applications where the visual stream is unreliable or completely absent. We propose a new paradigm for speech enhancement by exploiting recent breakthroughs in speech-driven lip synthesis. Using one such model as a teacher network, we train a robust student network to produce accurate lip movements that mask away the noise, thus acting as a "visual noise filter". The intelligibility of the speech enhanced by our pseudo-lip approach is comparable (< 3% difference) to the case of using real lips. This implies that we can exploit the advantages of using lip movements even in the absence of a real video stream. We rigorously evaluate our model using quantitative metrics as well as human evaluations. Additional ablation studies and a demo video on our website containing qualitative comparisons and results clearly illustrate the effectiveness of our approach. We provide a demo video which clearly illustrates the effectiveness of our proposed approach on our website: \url{http://cvit.iiit.ac.in/research/projects/cvit-projects/visual-speech-enhancement-without-a-real-visual-stream}. The code and models are also released for future research: \url{https://github.com/Sindhu-Hegde/pseudo-visual-speech-denoising}.

Visual Speech Enhancement Without A Real Visual Stream: An Expert Overview

The paper "Visual Speech Enhancement Without A Real Visual Stream" presents an innovative approach to speech enhancement by utilizing a pseudo-visual stream generated through speech-driven lip synthesis. The research aims to overcome a significant limitation in existing audio-visual speech enhancement techniques, which require a reliable visual stream of lip movements. In contrast, this paper introduces an approach that can function effectively without such a stream, using synthetically generated lip movements to aid in enhancing noisy speech.

Core Contributions and Methodology

The authors propose a novel hybrid framework that leverages recent advancements in lip synthesis technology. Instead of utilizing real visual data, which may be unavailable or unreliable in various real-world scenarios, they train a lip synthesis model to generate lip movements corresponding to clean speech information extracted from noisy audio inputs. This pseudo-lip approach serves as a "visual noise filter," aiding the downstream task of speech enhancement.

To accomplish this, the authors design a student-teacher training paradigm. The teacher network, based on a robust lip synthesis model like Wav2Lip, generates accurate lip movements from clean speech. The student network learns to mimic this output using noisy speech as input, effectively filtering out noise by focusing on lip movement patterns that correlate with the speech content. Notably, the intelligibility and quality of speech enhanced using these generated lip motions were found to be comparable (less than 3% difference) to those achieved using real lip data.

Evaluation and Results

The model was rigorously evaluated using quantitative metrics, such as PESQ and STOI, which assess speech quality and intelligibility, respectively. The authors also conducted human evaluations to validate the model's performance in real-world environments. The results consistently demonstrated the model's ability to outperform audio-only methods across various noise levels and types, and its performance closely approximated that of a realistic visual stream method.

Furthermore, the research highlights the model's robustness to different scenarios involving unseen noises and speakers, indicating its potential generalizability. The performance consistency across various speaker identities, languages, and accents further underscores the method's versatility.

Practical and Theoretical Implications

Practically, this approach opens new avenues for deploying speech enhancement systems in environments where visual streams are compromised or absent, such as poor-quality video calls, dynamic vlogs, and archival content enhancement. It accelerates progress beyond the current constraints faced by audio-visual methods, broadening applicability in scenarios previously dominated by audio-only solutions.

Theoretically, this work contributes to the ongoing exploration of multimodal fusion in machine learning, particularly in scenarios with missing modalities. It showcases how synthesized data can effectively substitute for real sensor data, potentially informing methodologies in other domains where capturing complete multimodal information is challenging.

Future Prospects in AI

The introduction of pseudo-visual methods may inspire future research to further refine artificial modalities as reliable proxies in machine learning applications. As lip synthesis technologies and understanding of multimodal interactions progress, we can anticipate the development of more advanced applications across areas such as automatic speech recognition (ASR), human-computer interaction, and beyond. Future explorations could also focus on enhancing the realism of synthesized streams and their integration with AI models, paving the way for more robust and adaptive multimodal systems.

In summary, this paper makes a significant contribution to the field by challenging the traditional reliance on real visual streams for speech enhancement and paves the way for future research and application development in multimodal AI systems.