Emotion-Aware Neural Transducer for Fine-Grained Speech Emotion Recognition

Core Concepts

The authors propose Emotion Neural Transducer (ENT) and its factorized variant (FENT) to enable fine-grained speech emotion recognition by jointly modeling acoustic and linguistic information through neural transducer architecture.

Abstract

The authors present two key components of the Emotion Neural Transducer (ENT) model: Emotion Joint Network: This extends the typical neural transducer architecture by adding an emotion joint network to integrate representations from the acoustic encoder and linguistic predictor, enabling the modeling of emotion categorical distribution through the alignment lattice. Lattice Max Pooling: To distinguish emotional and non-emotional frames in a weakly supervised setting, the authors propose a lattice max pooling loss that selects the nodes with the highest predicted probability of the target emotion and the minimum non-emotional probability. The authors further extend ENT to the Factorized Emotion Neural Transducer (FENT), which disentangles the blank symbol from vocabulary prediction and shares the predictor for both blank and emotion prediction. This allows the blank symbol to serve as an underlying indicator of emotion during inference. Experiments on the IEMOCAP dataset show that the ENT models outperform state-of-the-art utterance-level speech emotion recognition methods, while also achieving low word error rates. The authors also validate the fine-grained emotion modeling capability of their approaches on the ZED speech emotion diarization dataset.

Stats

The IEMOCAP dataset contains utterances annotated with single emotion category labels. The ZED dataset includes utterances annotated with emotional boundaries for each.

Quotes

None.

Key Insights Distilled From

Emotion Neural Transducer for Fine-Grained Speech Emotion Recognition

by Siyuan Shen,... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19224.pdf

Deeper Inquiries

How can the proposed ENT and FENT models be extended to handle more complex emotional dynamics, such as the co-occurrence of multiple emotions within a single utterance

To handle more complex emotional dynamics like the co-occurrence of multiple emotions within a single utterance, the ENT and FENT models can be extended in several ways. One approach could involve incorporating a hierarchical modeling structure that allows for the recognition of primary and secondary emotions. This hierarchical approach would enable the models to capture the nuanced emotional transitions and co-occurrences within the speech signal. Additionally, the models could be enhanced with attention mechanisms that dynamically focus on different emotional cues throughout the utterance, enabling them to adapt to the varying emotional dynamics present. Furthermore, the models could benefit from incorporating contextual information from previous utterances or speaker history. By considering the speaker's emotional state in previous interactions or the context of the conversation, the models can better understand and predict the co-occurrence of multiple emotions. This contextual information can provide valuable insights into the speaker's emotional trajectory and help the models interpret complex emotional patterns more accurately.

What other types of linguistic information, beyond transcripts, could be leveraged to further improve the fine-grained speech emotion recognition performance

Beyond transcripts, there are several types of linguistic information that could be leveraged to enhance fine-grained speech emotion recognition performance. One valuable source of information is prosody, which includes features like intonation, pitch, and rhythm of speech. Prosodic cues play a crucial role in conveying emotions and can provide additional context for emotion recognition. By incorporating prosodic features into the models, they can better capture the subtle variations in speech that indicate different emotional states. Another type of linguistic information that can be utilized is semantic content and sentiment analysis. By analyzing the semantic content of the speech and extracting sentiment information, the models can gain a deeper understanding of the speaker's emotional expression. This information can help disambiguate emotional cues and improve the accuracy of emotion recognition. Additionally, non-verbal cues such as facial expressions, gestures, and body language can be integrated into the models for a more comprehensive multimodal approach to emotion recognition. By combining linguistic information with non-verbal cues, the models can capture a broader range of emotional signals and enhance the overall performance of speech emotion recognition systems.

How can the insights from this work on joint speech recognition and emotion modeling be applied to other multimodal tasks, such as emotion recognition in videos or conversations

The insights gained from joint speech recognition and emotion modeling can be applied to other multimodal tasks, such as emotion recognition in videos or conversations, in several ways. One application is in video-based emotion recognition, where the models can leverage visual cues from facial expressions, body language, and scene context in addition to speech signals. By integrating speech and visual modalities, the models can capture a more holistic representation of emotional cues and improve the accuracy of emotion recognition in videos. In conversational settings, the joint modeling approach can be extended to analyze the emotional dynamics between speakers during interactions. By incorporating speech signals from multiple speakers, along with contextual information and turn-taking patterns, the models can infer the emotional states of each participant and the overall emotional trajectory of the conversation. This can be valuable for applications like sentiment analysis in customer service interactions or emotional understanding in group discussions. Furthermore, the principles of joint modeling can be adapted to other multimodal tasks beyond emotion recognition, such as audio-visual speech recognition or gesture recognition. By combining different modalities and leveraging the synergies between them, the models can achieve more robust and accurate performance in complex multimodal tasks.