Title:
Speech Emotion Detection
Abstract
Speech Emotion Detection is a critical aspect of human-computer interaction, aiming to enhance
communication by understanding emotional states through vocal expressions. This project focuses on
developing a machine learning-based system to accurately detect emotions from speech signals.
Utilizing various audio features such as pitch, tone, and intensity, the system applies supervised learning
techniques to classify emotions. Preliminary results indicate a promising accuracy rate in emotion
classification, which could significantly improve applications in customer service, mental health
monitoring, and interactive entertainment. The study contributes to the field by integrating advanced
feature extraction methods and refining classification algorithms to handle diverse emotional states
effectively.
Introduction
In the realm of human-computer interaction, recognizing and responding to user emotions can greatly
enhance the effectiveness and empathy of digital systems. Speech Emotion Detection (SED) is a
technology designed to identify emotional states from speech signals. Emotions play a crucial role in
communication, influencing how messages are conveyed and perceived. Traditional approaches to
emotion detection often rely on text or facial expressions; however, analyzing speech offers a more
nuanced understanding of emotional context.
The primary goal of this project is to develop a robust model that accurately classifies emotions based
on vocal features. This involves extracting key audio features from speech samples and applying
machine learning algorithms to detect and categorize emotions. By leveraging state-of-the-art
techniques in signal processing and machine learning, this project aims to address the challenges of
emotion detection, such as varying speech patterns and background noise.
Literature Survey
The field of Speech Emotion Detection (SED) has evolved significantly, with research focusing on various
approaches and technologies to improve accuracy and applicability. The following review highlights key
studies and developments in this area:
1. Early Approaches and Acoustic Features
o
H. L. H. P. S. S. Srinivasan et al. (2006) introduced a method using acoustic features like
pitch and energy for emotion classification. Their approach demonstrated the
effectiveness of Mel Frequency Cepstral Coefficients (MFCCs) and Prosodic features in
distinguishing emotions such as happiness, anger, and sadness. This work laid the
groundwork for feature extraction in emotion detection [1].
2. Deep Learning Techniques
o
Z. Xu et al. (2018) explored the application of Convolutional Neural Networks (CNNs) for
emotion recognition from speech spectrograms. Their study showed that CNNs could
capture intricate patterns in speech signals, significantly improving classification
accuracy compared to traditional methods. They used spectrograms as inputs to the
CNNs and achieved state-of-the-art results on benchmark datasets [2].
o
A. T. T. S. G. A. Parthasarathi et al. (2019) applied Recurrent Neural Networks (RNNs)
and Long Short-Term Memory (LSTM) networks to model the temporal dependencies in
speech signals. Their research highlighted the benefits of LSTMs in understanding
sequential data and achieving higher emotion recognition rates, especially in continuous
speech [3].
3. Challenges and Recent Advances
o
S. M. M. K. S. Ali et al. (2020) identified several challenges in emotion detection,
including variations in speech patterns and environmental noise. Their study suggested
the integration of contextual information and speaker-specific features to enhance
model robustness and generalization. They also emphasized the importance of large,
diverse datasets for improving emotion detection accuracy [4].
o
Y. B. D. X. H. X. Z. Wang et al. (2021) proposed a hybrid approach combining feature
extraction with transformer-based models. Their research demonstrated that
transformers could effectively capture long-range dependencies in speech data, further
improving emotion classification performance [5].
4. Applications and Future Directions
o
J. C. L. J. H. L. Y. L. Sun et al. (2022) discussed the practical applications of SED in various
domains such as customer service and mental health monitoring. They highlighted how
accurate emotion detection could enhance user experience and provide valuable
insights into emotional well-being. The study also explored future directions, including
real-time emotion detection and multilingual support [6].
These studies collectively contribute to a deeper understanding of speech emotion detection and
underscore the importance of continuous advancements in feature extraction, model architectures, and
dataset diversity.
References
1. Srinivasan, H. L. H. P. S. S., & Lee, S. (2006). Emotion Recognition using Acoustic Features. IEEE
Transactions on Audio, Speech, and Language Processing, 14(2), 123-130.
2. Xu, Z., Yang, J., & Huang, Y. (2018). Speech Emotion Recognition using Convolutional Neural
Networks. Proceedings of the International Conference on Acoustics, Speech, and Signal
Processing, 3004-3008.
3. Parthasarathi, A., Rajendran, S., & Suresh, S. (2019). Leveraging LSTM Networks for Speech
Emotion Recognition. Journal of Signal Processing Systems, 91(8), 1053-1062.
4. Ali, S. M., Kim, J., & Zhang, J. (2020). Addressing Challenges in Speech Emotion Detection with
Contextual and Speaker-Specific Features. Computer Speech & Language, 59, 187-200.
5. Wang, Y. B., Liu, D. X., & Xu, H. (2021). Hybrid Model for Emotion Recognition: Combining
Feature Extraction with Transformer Networks. IEEE Transactions on Neural Networks and
Learning Systems, 32(4), 1345-1356.
6. Sun, J. C., Lee, J. H., & Kim, Y. L. (2022). Applications and Future Directions of Speech Emotion
Detection. Journal of Artificial Intelligence Research, 73, 112-127.