Improved Long-range Dependency Capture in Speech Sentiment Recognition: a Comparative Study of Mfcc-bilstm and Mfcc-lstm
Year: 2025
Type: article
Abstract: The accurate analysis of sentiments conveyed through spoken language is of paramount importance in the domains of human-computer interaction.Speech sentiment recognition requires capturing long-range temporal dependencies within the speech signal.This study investigates the effectiveness of Bidirectional Long Short-Term Memory Networks (BiLSTMs) in capturing these dependencies compared to traditional unidirectional LSTMs.A hybrid approach combining Mel-Frequency Cepstral Coefficients (MFCCs) with BiLSTMs for speech sentiment analysis is proposed in this research work.The performance of the MFCC-BiLSTM model is evaluated and compared with that of an MFCC-LSTM model on TESS dataset.Experimental results demonstrate that the MFCC-BiLSTM model consistently outperforms the MFCC-LSTM model across various evaluation metrics, indicating improved accuracy of 98% in capturing long-range dependencies.The findings highlight the significance of bidirectional processing for enhancing speech sentiment recognition and provide valuable insights for developing more robust and accurate emotion recognition systems.
Authors: Suman Lata, Pardeep Sangwan, Dinesh Sheoran, Prachi Chaudhary
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4414816034
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