Fusion Approaches for Emotion Recognition from Speech Using Acoustic and Text-Based Features

Core Concepts

Different fusion approaches for emotion recognition from speech using acoustic and text-based features show improved performance with BERT embeddings compared to Glove embeddings.

Abstract

The content discusses various fusion approaches for emotion recognition from speech using acoustic and text-based features. It highlights the use of BERT embeddings over Glove embeddings, the impact of different training strategies, and the importance of defining folds in datasets like IEMOCAP. The study compares models on IEMOCAP and MSP-PODCAST datasets, showcasing the benefits of combining audio and text information for emotion recognition. Models: Individual models for audio and text-based systems are described. Fusion models using early and late fusion techniques are explored. Different training strategies for fusion models are discussed. Experimental Setup and Datasets: Experiments were conducted on IEMOCAP and MSP-PODCAST datasets. The impact of fold criteria on IEMOCAP results is analyzed. Details on data preprocessing, model training, and evaluation metrics are provided. Results and Discussion: Performance results for audio, Glove, and BERT-based text models are compared. Fusion models show significant improvements in emotion recognition. The effect of using BERT embeddings over Glove embeddings is highlighted. The importance of seed selection in model training is emphasized. Conclusions: The study demonstrates the benefits of using BERT embeddings and fusing audio-text modalities for emotion recognition. Recommendations for defining folds in datasets like IEMOCAP are proposed. The impact of different training strategies and fusion techniques is discussed.

Stats

We propose to obtain contextualized word embeddings with BERT to represent the information contained in speech transcriptions. Fusing acoustic and text-based systems is beneficial on both datasets. The AvRec improves by 16% relative to the best performing single model when combining audio and text information.

Quotes

"We demonstrated the positive effect of representing linguistic information using contextualized word embeddings extracted with BERT compared to using standard word embeddings like those extracted with Glove." "Adding text information to audio-based SER systems gives significant performance improvements."

Key Insights Distilled From

Fusion approaches for emotion recognition from speech using acoustic and text-based features

by Leonardo Pep... at arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18635.pdf

Deeper Inquiries

How can the fusion of audio and text information be further optimized for emotion recognition systems

To further optimize the fusion of audio and text information for emotion recognition systems, several strategies can be considered: Feature Engineering: Explore more advanced feature extraction techniques for both audio and text modalities to capture a wider range of emotional cues. This could involve incorporating prosodic features, sentiment analysis of text, or even non-verbal cues from speech. Model Architecture: Experiment with more complex neural network architectures that can effectively integrate audio and text information. This could include attention mechanisms to focus on relevant parts of the input data or hierarchical fusion models to capture interactions at different levels. Data Augmentation: Increase the diversity of the training data by augmenting the audio and text samples with variations in emotion expression, speech styles, or linguistic content. This can help the model generalize better to unseen data. Multi-Modal Pretraining: Pretrain the fusion model on a large multi-modal dataset to learn representations that capture the relationships between audio and text features. This can help the model leverage shared information between the modalities more effectively.

What are the potential implications of using different fold criteria in datasets like IEMOCAP on the performance of text-based systems

The choice of fold criteria in datasets like IEMOCAP can have significant implications on the performance of text-based systems: Impact on Generalization: Using different fold criteria can affect how well the text-based system generalizes to unseen data. By avoiding repeating scripted dialogues between training and test sets, the model is forced to learn more robust features that are not specific to the training instances. Avoiding Overfitting: Defining folds based on speakers or scripts can prevent the text-based system from memorizing specific instances, leading to a more realistic estimation of performance. This helps in evaluating the true capabilities of the model in real-world scenarios. Enhancing Model Robustness: By ensuring that the test data contains novel instances not encountered during training, the text-based system is challenged to learn more abstract and context-independent representations, improving its robustness and applicability.

How can the findings of this study be applied to real-world applications beyond emotion recognition from speech

The findings of this study can be applied to real-world applications beyond emotion recognition from speech in various ways: Human-Computer Interaction: Implementing emotion recognition systems in devices like virtual assistants or chatbots can enhance user experience by enabling more personalized interactions based on emotional cues from speech. Customer Feedback Analysis: Companies can utilize emotion recognition technology to analyze customer feedback from calls, surveys, or social media to gauge sentiment and improve customer satisfaction. Mental Health Monitoring: Emotion recognition systems can be integrated into mental health apps to track and analyze changes in emotional states based on speech patterns, providing valuable insights for therapists and individuals. Educational Tools: Emotion recognition can be used in educational settings to assess student engagement and emotional responses during online learning, helping educators tailor their teaching methods accordingly.