- The paper introduces TS-TalkNet, incorporating pre-enrolled speaker embeddings to significantly improve active speaker detection.
- It employs audio and visual temporal encoders with cross-attention and transformer-based self-attention to robustly synchronize multi-modal inputs.
- Experimental results on AVA and ASW datasets show marked improvements in mAP, AP, AUC, and reduced EER, affirming its efficacy in complex settings.
Target Active Speaker Detection with Audio-visual Cues: A Comprehensive Analysis
The paper introduces Target Speaker TalkNet (TS-TalkNet), an innovative approach for enhancing active speaker detection (ASD) by integrating audio-visual cues with pre-enrolled speaker embeddings. Traditional ASD methodologies predominantly focus on audio-visual synchronization, relying heavily on the visual quality of lip regions for effective detection. However, TS-TalkNet extends this paradigm by leveraging a pre-enrolled speaker model to improve detection accuracy, particularly in real-world scenarios where high-resolution video may not be available.
Methodology and Framework
TS-TalkNet integrates audio-visual cues with target speaker embeddings through a multi-stage process:
- Feature Representation Frontend:
- Utilizes a visual temporal encoder to capture long-term facial dynamics.
- Incorporates an audio temporal encoder based on the ResNet34 architecture with a squeeze-and-excitation (SE) module to derive audio embeddings.
- Employs a speaker encoder, specifically a pre-trained ECAPA-TDNN model, to generate robust speaker embeddings from pre-enrolled speech data.
- Speaker Detection Backend:
- Employs a cross-attention module to achieve effective audio-visual synchronization.
- Merges speaker embeddings with the synchronized audio-visual embeddings using various fusion strategies, including direct concatenation and cross-attention.
- Utilizes a self-attention mechanism, modeled after the transformer architecture, to capture temporal dependencies and refine the ASD output.
- Training and Loss: A cross-entropy loss function is applied to optimize frame-level ASD predictions, enhancing model accuracy on spoken vs. non-spoken labels.
Experimental Results
TS-TalkNet's empirical evaluation on the AVA-ActiveSpeaker and Active Speakers in the Wild (ASW) datasets demonstrates a clear enhancement in ASD performance. On the AVA dataset, TS-TalkNet achieved an mAP of 93.9%, marking an improvement of 1.6% over the TalkNet baseline. On the ASW test set, TS-TalkNet enhanced performance by 0.8% in AP, 0.4% in AUC, and reduced EER by 0.8%, underscoring its efficacy in complex acoustic conditions with varying video qualities.
Implications and Future Directions
The introduction of target speaker embeddings in ASD represents a significant methodological advancement that addresses the limitations of conventional audio-visual synchronization-dependent approaches. TS-TalkNet’s capability to integrate speaker characteristics provides a distinct advantage in environments with acoustic complexities and visual constraints. This development signals a shift towards more flexible, robust ASD frameworks capable of adapting to varying real-world conditions.
Moving forward, further refinement of speaker fusion mechanisms can be explored to optimize the integration of audio-visual and speaker cues. Additionally, expanding TS-TalkNet’s application to other speech-related tasks, such as speaker verification and diarization, could prove fruitful, potentially leading to more comprehensive multi-modal speech processing systems.
In summary, TS-TalkNet not only advances the capabilities of ASD frameworks but also enriches the broader field of multi-modal audio-visual processing, offering researchers novel avenues for exploration and application.