A Hybrid DL Model with IFS and Fuzzy Feature Selection for Emotion Recognition Using Sequential Architecture

Problem Definition

The field of Speech Emotion Recognition (SER) faces numerous challenges, with one major issue being the difficulty in accurately capturing emotional content from speech signals. Existing models, despite leveraging deep learning techniques, struggle to achieve high accuracy rates, ranging between only 60 to 85%. This limitation underscores the pressing need for more effective feature extraction methods to improve the discriminative power of SER systems. Moreover, the processing and analysis of variable-length utterances present challenges, further complicating the accurate recognition of emotions in speech. Another critical issue is the presence of imbalanced datasets, where certain emotion classes are underrepresented, leading to biased results and inaccurate classification across all categories.

In light of these challenges, there is a clear necessity for the development of advanced SER models that integrate effective feature extraction, feature selection, and classification methods to enhance the overall performance and reliability of emotion recognition systems.

Objective

The objective is to enhance the accuracy of Speech Emotion Recognition (SER) systems by developing a new approach based on a sequential Deep Learning (DL) architecture. This approach involves implementing data scaling techniques, extracting features using Mel-spectrogram, utilizing a DL architecture with multiple layers, and incorporating an Information Gain-based Feature Selection (IFS) model combined with a Fuzzy system. The goal is to improve feature selection, reduce complexity, overcome dataset dimensionality issues, and effectively classify the seven emotion classes present in audio signals.

Proposed Work

With the aim of improving the accuracy rate of Speech Emotion Recognition (SER) systems, a new approach based on a sequential Deep Learning (DL) architecture has been developed to recognize seven emotions in audio signals. The model analyzes the features of audio signals to determine and classify the emotions of a person. Before extracting the feature data, a data scaling technique is implemented on the audio signals to scale the data based on size and duration. Mel-spectrogram is then applied to capture spectral and temporal features of the audio signals, transforming the audio signals from time domain to frequency domain using Fast Fourier Transform (FFT). Additionally, a DL architecture with multiple layers is utilized to extract intricate features from the audio signals.

To further enhance the feature selection process, an advancement has been made in the Feature Selection (FS) phase by incorporating an Information Gain-based Feature Selection (IFS) model combined with a Fuzzy system. The IFS-Fuzzy based model is used to select important and informative features, reducing complexity and overcoming dataset dimensionality issues. The IFS calculates the feature score which serves as input to the fuzzy system. The fuzzy system evaluates this feature score based on predefined rules to determine the feature's degree as low, medium, or high, ultimately deciding its inclusion or exclusion in the final feature list. Lastly, a DL sequential layered network is developed with three layers (input, hidden, and output) to effectively process the data, improve the model's performance, and classify the seven emotions classes accurately.

Application Area for Industry

This project can be beneficially applied in various industrial sectors such as customer service, healthcare, social media, entertainment, education, and marketing. In customer service, the advanced SER model can be used to analyze customer feedback and sentiments, enabling companies to improve their services and products based on the emotions expressed. Within healthcare, the model can assist in monitoring patients' emotional states and providing timely interventions when needed. In social media and entertainment, the model can be used to analyze user emotions and preferences, allowing for personalized content recommendations. Furthermore, in education, the model can aid in assessing students' engagement and understanding during online learning sessions.

Lastly, in marketing, the model can help companies understand consumer emotions towards their products or campaigns, enabling them to tailor their strategies accordingly. By implementing the proposed solutions of effective feature extraction, feature selection, and deep learning architecture, industries can significantly benefit from improved accuracy in emotion classification, leading to better decision-making and enhanced customer satisfaction.

Application Area for Academics

The proposed project on Speech Emotion Recognition (SER) can significantly enrich academic research, education, and training in the field of artificial intelligence and machine learning. By addressing the limitations of current SER systems, such as feature extraction challenges, imbalanced datasets, and inconsistent processing of variable-length utterances, the project aims to develop a more accurate and effective model for emotion classification in audio signals. In academic research, the project offers a novel approach using sequential deep learning architecture, mel-spectrogram analysis, and fuzzy-IFS feature selection techniques to enhance the discriminative power of SER systems. This research can contribute to advancing the current state-of-the-art in emotion recognition technology and provide valuable insights for researchers working in the field of speech processing and affective computing. For education and training purposes, the project provides a practical demonstration of advanced machine learning techniques applied to real-world audio data.

Students pursuing degrees in data science, artificial intelligence, or related fields can benefit from exploring the project's codebase, literature, and methodology to enhance their understanding of deep learning, feature engineering, and emotion recognition algorithms. Specifically, researchers, MTech students, and PhD scholars working in the domains of natural language processing, audio signal processing, and affective computing can leverage the code and findings of this project for further experimentation, validation, and extension of the proposed SER model. The utilization of algorithms like Melspectrum, Fuzzy-IFS, and ConvLSTMNet can inspire future research directions and foster interdisciplinary collaborations in exploring innovative research methods, simulations, and data analysis techniques within educational settings. In conclusion, the proposed project on Speech Emotion Recognition has the potential to contribute significantly to academic research, education, and training by addressing key challenges in emotion classification from audio signals. Its relevance lies in advancing the field of artificial intelligence, enhancing research methodologies, and empowering students and researchers to explore new frontiers in machine learning and affective computing.

Future Scope: The future scope of this project includes expanding the emotion recognition capabilities to include additional emotional states, developing more robust feature extraction techniques, enhancing the model's performance on challenging datasets, and exploring the application of transfer learning and ensemble methods for improved classification accuracy. Furthermore, the integration of multimodal data sources, such as text and facial expressions, can be explored to create more comprehensive emotion recognition systems with real-world applications in human-computer interaction, mental health assessment, and sentiment analysis.

Algorithms Used

The project utilized three algorithms to improve the accuracy rate of Speech Emotion Recognition (SER) systems. The Melspectrum algorithm was first applied to extract spectral and temporal features from audio signals, converting them from the time domain to the frequency domain using FFT. Next, the Fuzzy-IFS algorithm was utilized to select important features and reduce complexity by determining feature importance based on a calculated feature score passed through a fuzzy system. Finally, the ConvLSTMNet algorithm, a sequential deep learning (DL) network with input, hidden, and output layers, was developed to classify emotions in audio signals based on the extracted features and target labels. The DL network underwent training with the training data and was evaluated using testing data to accurately detect and classify seven emotion classes.

Keywords

SEO-optimized keywords: SER systems, emotion recognition, feature extraction, deep learning techniques, variable-length utterances, imbalanced datasets, emotion classification, sequential DL architecture, audio signals, mel-spectrogram, FFT, FS phase, IFS-Fuzzy model, feature selection, DL network, categorical data, training data, testing data, emotion classes, affective computing, speech analysis, emotion modeling, user preferences, human-computer interaction, affective computing algorithms.

SEO Tags

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