Deep Learning Based Emotion Recognition System Using Speech Features And Transcriptions

Abstract

This paper proposes a speech emotion recognition method based on speech features and speech transcriptions (text). Speech features such as Spectrogram and Mel-frequency Cepstral Coefficients (MFCC) help retain emotion-related low-level characteristics in speech whereas text helps capture semantic meaning, both of which help in different aspects of emotion detection. We experimented with several Deep Neural Network (DNN) architectures, which take in different combinations of speech features and text as inputs. The proposed network architectures achieve higher accuracies when compared to state-of-the-art methods on a benchmark dataset. The combined MFCC-Text Convolutional Neural Network (CNN) model proved to be the most accurate in recognizing emotions in IEMOCAP data.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keytripathi2019deep

Related papers

• Emodiarize: Speaker Diarization And Emotion Identification From Speech Signals Using Convolutional Neural Networks (2023)0.00
• Emotech: A Multi-modal Speech Emotion Recognition Using Multi-source Low-level Information With Hybrid Recurrent Network (2025)8.35
• A Transfer Learning Method For Speech Emotion Recognition From Automatic Speech Recognition (2020)0.00
• Multimodal Speech Emotion Recognition Using Audio And Text (2018)18.02
• Attention Based Fully Convolutional Network For Speech Emotion Recognition (2018)15.25
• Emotion Recognition From Speech (2019)0.00
• Fusion Approaches For Emotion Recognition From Speech Using Acoustic And Text-based Features (2024)12.25
• Light-sernet: A Lightweight Fully Convolutional Neural Network For Speech Emotion Recognition (2021)14.90