Multi-Channel Speech Enhancement for Cocktail Party Speech Emotion Recognition
Abstract
This paper highlights the critical importance of multi-channel speech enhancement (MCSE) for speech emotion recognition (ER) in cocktail party scenarios. A multi-channel speech dereverberation and separation front-end integrating DNN-WPE and mask-based MVDR is used to extract the target speaker's speech from the mixture speech, before being fed into the downstream ER back-end using HuBERT- and ViT-based speech and visual features. Experiments on mixture speech constructed using the IEMOCAP and MSP-FACE datasets suggest the MCSE output consistently outperforms domain fine-tuned single-channel speech representations produced by: a) Conformer-based metric GANs; and b) WavLM SSL features with optional SE-ER dual task fine-tuning. Statistically significant increases in weighted, unweighted accuracy and F1 measures by up to 9.5%, 8.5% and 9.1% absolute (17.1%, 14.7% and 16.0% relative) are obtained over the above single-channel baselines. The generalization of IEMOCAP trained MCSE front-ends are also shown when being zero-shot applied to out-of-domain MSP-FACE data.
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Research summary
This paper makes a significant contribution by demonstrating the effectiveness of multi-channel speech enhancement techniques for improving emotion recognition in challenging acoustic environments. The innovative methodology and strong experimental results highlight its potential impact on future research and applications in the field.
Methodology
The paper presents a robust methodology that integrates multi-channel speech enhancement (MCSE) with emotion recognition (ER) in cocktail party scenarios. The use of a DNN-WPE based dereverberation and mask-based MVDR separation front-end is innovative, particularly in its application to ER, which has traditionally relied on single-channel inputs. The integration of HuBERT and ViT for feature extraction further enhances the approach, making it suitable for both audio-only and audio-visual ER systems. The detailed ablation studies provide insights into the contributions of each component, showcasing a comprehensive understanding of the problem space.
Results & evaluation
The experiments are well-structured, utilizing two established datasets (IEMOCAP and MSP-FACE) to evaluate the proposed system's performance. The results demonstrate statistically significant improvements in accuracy and F1 scores compared to single-channel baselines, indicating the effectiveness of the MCSE approach. The zero-shot application of the MCSE front-end to out-of-domain data is particularly noteworthy, suggesting good generalization capabilities.
Reproducibility
The paper provides sufficient details regarding the experimental setup, including model configurations and training strategies, which enhances reproducibility. However, the absence of a publicly available code repository may hinder full reproducibility for other researchers.
Limitations
While the paper addresses a significant gap in the literature, it does not explore the potential computational costs and real-time applicability of the proposed MCSE front-end in practical scenarios. Additionally, the reliance on simulated data for training may limit the model's performance in real-world applications.
Broader impact
The findings of this research have the potential to significantly advance the field of emotion recognition in noisy environments, particularly in applications such as human-computer interaction, assistive technologies, and surveillance systems. The integration of multi-channel processing could lead to more robust systems capable of understanding human emotions in complex auditory scenes. This paper makes a significant contribution by demonstrating the effectiveness of multi-channel speech enhancement techniques for improving emotion recognition in challenging acoustic environments. The innovative methodology and strong experimental results highlight its potential impact on future research and applications in the field.
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