Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection (2107.06592v2)

Abstract: Active speaker detection (ASD) seeks to detect who is speaking in a visual scene of one or more speakers. The successful ASD depends on accurate interpretation of short-term and long-term audio and visual information, as well as audio-visual interaction. Unlike the prior work where systems make decision instantaneously using short-term features, we propose a novel framework, named TalkNet, that makes decision by taking both short-term and long-term features into consideration. TalkNet consists of audio and visual temporal encoders for feature representation, audio-visual cross-attention mechanism for inter-modality interaction, and a self-attention mechanism to capture long-term speaking evidence. The experiments demonstrate that TalkNet achieves 3.5% and 2.2% improvement over the state-of-the-art systems on the AVA-ActiveSpeaker dataset and Columbia ASD dataset, respectively. Code has been made available at: https://github.com/TaoRuijie/TalkNet_ASD.

Overview of "Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection"

Active Speaker Detection (ASD) is crucial for applications such as audio-visual speech recognition and speaker tracking. The paper "Is Someone Speaking? Exploring Long-term Temporal Features for Audio-visual Active Speaker Detection" introduces a novel framework, TalkNet, that considers both short-term and long-term features to enhance ASD performance.

Framework and Methodology

TalkNet leverages temporal dynamics and intricate audio-visual interactions to improve decision accuracy:

• Architecture: It features audio and visual temporal encoders to encapsulate feature representation, an audio-visual cross-attention mechanism for inter-modality interaction, and a self-attention system to capture long-term evidence. These components together address the fluid and dynamic nature of speaking activities.
• Feature Representation: Short-term segment approaches (e.g., 200-600 ms) dominate current paradigms. TalkNet distinguishes itself by focusing on longer sequences to extract robust speaking evidence.
• Attention Mechanisms: By integrating cross-attention and self-attention layers, TalkNet is able to align and synthesize audio-visual data, enhancing the detection capability through more comprehensive temporal contexts.

Experimental Validation

The results on the AVA-ActiveSpeaker and Columbia ASD datasets are noteworthy:

• TalkNet achieved improvements of 3.5% on the AVA-ActiveSpeaker dataset and 2.2% on the Columbia ASD dataset over existing state-of-the-art methods. These gains illustrate the efficacy of employing long-term temporal features and advanced attention mechanisms in challenging real-world scenarios.
• The use of an innovative negative sampling technique for audio augmentation further improved noise robustness, demonstrating TalkNet's adaptability in noisy environments without necessitating external noise datasets.

Implications and Future Directions

The implications of this work are multifaceted:

• Practical Applications: Improved ASD can enhance the performance of applications that rely on accurate speaker detection, such as automatic video subtitling and conference transcription.
• Theoretical Advancements: The results highlight the importance of integrating long-term temporal dynamics and cross-modal interactions, encouraging future research in similar multimodal tasks.

As ASD technologies continue to evolve, several avenues for future research arise:

• Integration with Other Modalities: Expanding TalkNet to incorporate additional modalities (e.g., textual data) could further refine speaker detection accuracy.
• Scalability and Efficiency: Future work might explore lightweight versions of TalkNet suitable for deployment on resource-constrained devices.
• Real-world Adaptation: Continued exploration into how TalkNet can be optimized for diverse and unpredictable real-world conditions remains a necessary pursuit.

Conclusion

The paper provides clear evidence of the benefits of utilizing long-term audio-visual features and attention mechanisms in ASD tasks. TalkNet sets a new benchmark by effectively addressing the limitations of short-segment approaches, thus paving the way for advanced developments in active speaker technologies.