A Two-stage Framework for Robust Speech Emotion Recognition by Extracting Target Speaker from Human Speech Noise

Основные понятия

A novel two-stage framework that cascades target speaker extraction and speech emotion recognition to mitigate the impact of human speech noise on emotion recognition performance.

Аннотация

The paper proposes a two-stage framework for robust speech emotion recognition (SER) in noisy environments, particularly those with human speech noise.

In the first stage, the framework trains a target speaker extraction (TSE) model to extract the speech of the target speaker from a mixture of speech signals. This TSE model is trained on a large-scale mixed-speech corpus.

In the second stage, the extracted target speaker speech is used for SER training and testing. Two training methods are explored:

1. TSE-SER-base: The pretrained TSE model is used to denoise the emotional speech corpus, and the denoised corpus is then used to train the SER model.

2. TSE-SER-ft: The pretrained TSE model is jointly fine-tuned with the SER model using the mixed emotional speech corpus.

Experiments show that the proposed framework significantly outperforms baseline SER models that do not use the TSE method, achieving up to a 14.33% improvement in unweighted accuracy. The framework is particularly effective in dealing with different-gender speech mixtures.

The key insights are:

• Human speech noise severely degrades the performance of SER models trained on clean data.
• Integrating TSE into the SER framework can effectively mitigate the impact of human speech noise.
• Joint fine-tuning of the TSE and SER models further improves the performance.
• The framework performs better on different-gender speech mixtures compared to same-gender mixtures.

Статистика

The SI-SDR of the noisy speech before being processed by the TSE model is 0.09 dB.
The SI-SDRi for the TSE models of TSE-SER-base and TSE-SER-ft are 7.68 dB and 12.90 dB, respectively.
The SI-SDR of same-gender mixture is 0 dB, while the SI-SDR of different-gender mixture is 0.02 dB.
The SI-SDRi for the TSE model on same-gender mixture is 1.09 dB, and on different-gender mixture is 5.22 dB.

Цитаты

"Developing a robust speech emotion recognition (SER) system in noisy conditions faces challenges posed by different noise properties."
"Most previous studies have not considered the impact of human speech noise, thus limiting the application scope of SER."
"Our developed system achieves a 14.33% improvement in unweighted accuracy (UA) compared to a baseline without using TSE method, demonstrating the effectiveness of our framework in mitigating the impact of human speech noise."

Ключевые выводы из

Two-stage Framework for Robust Speech Emotion Recognition Using Target Speaker Extraction in Human Speech Noise Conditions

by Jinyi Mi, Xi... в arxiv.org 10-01-2024

https://arxiv.org/pdf/2409.19585.pdf

Дополнительные вопросы

How can the proposed framework be extended to handle more complex noise environments, such as those with multiple noise sources or reverberant conditions?

To extend the proposed two-stage framework for robust Speech Emotion Recognition (SER) to handle more complex noise environments, several strategies can be implemented. First, the framework could incorporate advanced multi-channel audio processing techniques, which would allow for the separation of multiple overlapping noise sources. By utilizing spatial information from multiple microphones, the system could apply beamforming techniques to enhance the target speaker's signal while suppressing background noise.
Additionally, the integration of reverberation modeling could be beneficial. This could involve training the Target Speaker Extraction (TSE) model with simulated reverberant conditions, allowing it to learn how to effectively extract the target speaker's voice from echoes and reflections that are common in real-world environments. Techniques such as dereverberation algorithms could also be employed to preprocess the audio signals before they are fed into the TSE model.
Moreover, the framework could be enhanced by employing deep learning architectures that are specifically designed for complex acoustic environments, such as recurrent neural networks (RNNs) or attention-based models. These architectures can better capture temporal dependencies and contextual information, which are crucial for distinguishing between the target speaker and multiple noise sources.
Finally, incorporating a feedback mechanism that continuously learns from the noise characteristics in real-time could improve the adaptability of the framework. This would allow the system to dynamically adjust its parameters based on the specific noise conditions encountered during operation, thereby enhancing the overall robustness of the SER system.

What other auxiliary information, beyond the enrollment utterance, could be leveraged to further improve the target speaker extraction and the overall SER performance?

Beyond the enrollment utterance, several types of auxiliary information could be leveraged to enhance the performance of the Target Speaker Extraction (TSE) and the overall Speech Emotion Recognition (SER) system. One potential source of auxiliary information is the use of speaker characteristics, such as gender, age, or accent, which could be integrated into the TSE model to improve its ability to distinguish the target speaker's voice from background noise. This could be achieved by incorporating speaker embeddings that capture these characteristics, allowing the model to better focus on the target speaker's unique vocal traits.
Another valuable auxiliary input could be contextual information about the conversation or the emotional state of the speakers. For instance, metadata regarding the emotional context of the conversation (e.g., whether it is a heated discussion or a calm dialogue) could guide the TSE model in prioritizing certain emotional features during extraction. Additionally, using visual cues from video data, such as facial expressions or body language, could provide complementary information that enhances the understanding of the emotional context, thereby improving SER performance.
Furthermore, incorporating temporal information, such as the timing of speech segments or pauses, could help the model recognize patterns associated with different emotions. This could be particularly useful in distinguishing between emotions that may have similar acoustic features but differ in their temporal dynamics.
Lastly, leveraging external knowledge bases or emotional lexicons that provide insights into the emotional connotations of specific words or phrases could also enhance the SER system's ability to accurately classify emotions based on the extracted speech.

How would the framework's performance scale with larger and more diverse emotional speech datasets, and how could it be adapted to handle a wider range of emotion categories?

The performance of the proposed two-stage framework is likely to scale positively with larger and more diverse emotional speech datasets. As the volume of training data increases, the TSE model can learn more robust representations of the target speaker's voice, leading to improved extraction accuracy even in the presence of complex noise conditions. A diverse dataset that includes a wide range of emotional expressions, accents, and speaking styles would enable the model to generalize better across different scenarios, enhancing its adaptability and performance in real-world applications.
To effectively handle a wider range of emotion categories, the framework could be adapted in several ways. First, the SER model could be designed to incorporate multi-label classification techniques, allowing it to recognize and classify multiple emotions simultaneously. This would be particularly useful in scenarios where emotions are expressed in a nuanced manner, such as mixed emotions or emotional transitions.
Additionally, the framework could benefit from the implementation of transfer learning techniques, where a model pre-trained on a large, diverse dataset is fine-tuned on a smaller, specific dataset. This approach would allow the model to leverage the knowledge gained from the broader dataset while adapting to the specific emotional categories of interest.
Moreover, the integration of data augmentation techniques could help in expanding the effective size of the training dataset. By artificially generating variations of the existing emotional speech data (e.g., altering pitch, speed, or adding synthetic noise), the model can be trained to recognize emotions under various conditions, further enhancing its robustness.
Finally, continuous learning mechanisms could be employed, where the model is periodically updated with new data as it becomes available. This would ensure that the framework remains relevant and effective in recognizing emerging emotional expressions and categories, thereby maintaining high performance across diverse applications.