arXiv:2410.22350v1 Announce Type: new
Abstract: In this paper, we propose a quality-aware end-to-end audio-visual neural speaker diarization framework, which comprises three key techniques. First, our audio-visual model takes both audio and visual features as inputs, utilizing a series of binary classification output layers to simultaneously identify the activities of all speakers. This end-to-end framework is meticulously designed to effectively handle situations of overlapping speech, providing accurate discrimination between speech and non-speech segments through the utilization of multi-modal information. Next, we employ a quality-aware audio-visual fusion structure to address signal quality issues for both audio degradations, such as noise, reverberation and other distortions, and video degradations, such as occlusions, off-screen speakers, or unreliable detection. Finally, a cross attention mechanism applied to multi-speaker embedding empowers the network to handle scenarios with varying numbers of speakers. Our experimental results, obtained from various data sets, demonstrate the robustness of our proposed techniques in diverse acoustic environments. Even in scenarios with severely degraded video quality, our system attains performance levels comparable to the best available audio-visual systems.
Expert Commentary: A Quality-Aware End-to-End Audio-Visual Neural Speaker Diarization Framework
This paper presents a novel approach to audio-visual speaker diarization, which is the process of determining who is speaking when in an audio or video recording. Speaker diarization is a crucial step in various multimedia information systems, such as video conferencing, surveillance systems, and automatic transcription services. This research proposes a quality-aware end-to-end framework that leverages both audio and visual information to accurately identify and separate individual speakers, even in challenging scenarios.
The proposed framework is multi-disciplinary in nature, combining concepts from audio processing, computer vision, and deep learning. By taking both audio and visual features as inputs, the model is able to capture a broader range of information, leading to more accurate speaker discrimination. This multi-modal approach allows the system to handle situations with overlapping speech, where audio-only methods may struggle.
One key aspect of this framework is the quality-aware audio-visual fusion structure. It addresses signal quality issues that commonly arise in real-world scenarios, such as noise, reverberation, occlusions, and unreliable detection. By incorporating quality-aware fusion, the system can mitigate the negative effects of audio and video degradations, leading to more robust performance. This is particularly important in applications where the video quality may be compromised, as the proposed framework can still perform at high levels.
Another notable contribution of this research is the use of a cross attention mechanism applied to multi-speaker embedding. This mechanism enables the network to handle scenarios with varying numbers of speakers. This is crucial in real-world scenarios where the number of speakers may change dynamically, such as meetings or group conversations.
The experimental results presented in the paper demonstrate the effectiveness and robustness of the proposed techniques. The framework achieves competitive performance on various datasets, even in situations with severely degraded video quality. These results highlight the potential of leveraging both audio and visual information for speaker diarization tasks.
In the wider field of multimedia information systems, this research contributes to the advancement of audio-visual processing techniques. By combining audio and visual cues, the proposed framework enhances the capabilities of multimedia systems, enabling more accurate and reliable speaker diarization. This has implications for various applications, including video surveillance, automatic transcription services, and virtual reality systems.
Furthermore, the concepts presented in this paper have connections to other related fields such as animations, artificial reality, augmented reality, and virtual realities. The use of audio-visual fusion and multi-modal information processing can be applied to enhance user experiences in these domains. For example, in virtual reality, accurate audio-visual synchronization and speaker separation can greatly enhance the immersion and realism of virtual environments, leading to more engaging experiences for users.
In conclusion, this paper introduces a quality-aware end-to-end audio-visual neural speaker diarization framework that leverages multi-modal information and addresses signal quality issues. The proposed techniques demonstrate robust performance in diverse acoustic environments, highlighting the potential of combining audio and visual cues for speaker diarization tasks. This research contributes to the wider field of multimedia information systems and has implications for various related domains, such as animations, artificial reality, augmented reality, and virtual realities.