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TOWARD ROBUST SPEECH EMOTION RECOGNITION AND CLASSIFICATION USING NATURAL LANGUAGE PROCESSING WITH DEEP LEARNING MODEL
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AI
DOI：10.1142/S0218348X25400225.png)
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Abstract
En 中文
Speech Emotion Recognition (SER) plays a significant role in human-machine interaction applications. Over the last decade, many SER systems have been anticipated. However, the performance of the SER system remains a challenge owing to the noise, high system complexity and ineffective feature discrimination. SER is challenging and vital, and feature extraction is critical in SER performance. Deep Learning (DL)-based techniques emerge as proficient solutions for SER due to their competence in learning unlabeled data, superior capability of feature representation, capability to handle larger datasets and ability to handle complex features. Different DL techniques, like Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Deep Neural Network (DNN) and so on, are successfully presented for automated SER. The study proposes a Robust SER and Classification using the Natural Language Processing with DL (RSERC-NLPDL) model. The presented RSERC-NLPDL technique intends to identify the emotions in the speech signals. In the RSERC-NLPDL technique, pre-processing is initially performed to transform the input speech signal into a valid format. Besides, the RSERC-NLPDL technique extracts a set of features comprising Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), Harmonic-to-Noise Rate (HNR) and Teager Energy Operator (TEO). Next, selecting features can be carried out using Fractal Seagull Optimization Algorithm (FSOA). The Temporal Convolutional Autoencoder (TCAE) model is applied to identify speech emotions, and its hyperparameters are selected using fractal Sand Cat Swarm Optimization (SCSO) algorithm. The simulation analysis of the RSERC-NLPDL method is tested using a speech database. The investigational analysis of the RSERC-NLPDL technique showed superior accuracy outcomes of 94.32% and 95.25% under EMODB and RAVDESS datasets over other models in distinct measures.
Keywords:
Speech Emotion Recognition
Deep Learning
Fractal Seagull Optimization Algorithm
Feature Extraction
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