A Language-Independent Speech Sentiment Analysis Using Prosodic Features

Bansal, Monil; Yadav, Sampriti; Vishwakarma, Dinesh Kumar

doi:10.1109/iccmc51019.2021.9418357

Cited by 8 publications

(1 citation statement)

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“…An accuracy rate of 89% was reported using five different spectral features. A language independent speech emotion recognition system was designed using a custom dataset that was created by combining RAVDESS and TESS [ 42 ]. For that dataset, an accuracy rate of 78.28% was achieved.…”

Section: Literature Reviewmentioning

confidence: 99%

Speech Emotion Recognition Using Attention Model

Singh

Saheer

Faust

2023

IJERPH

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Speech emotion recognition is an important research topic that can help to maintain and improve public health and contribute towards the ongoing progress of healthcare technology. There have been several advancements in the field of speech emotion recognition systems including the use of deep learning models and new acoustic and temporal features. This paper proposes a self-attention-based deep learning model that was created by combining a two-dimensional Convolutional Neural Network (CNN) and a long short-term memory (LSTM) network. This research builds on the existing literature to identify the best-performing features for this task with extensive experiments on different combinations of spectral and rhythmic information. Mel Frequency Cepstral Coefficients (MFCCs) emerged as the best performing features for this task. The experiments were performed on a customised dataset that was developed as a combination of RAVDESS, SAVEE, and TESS datasets. Eight states of emotions (happy, sad, angry, surprise, disgust, calm, fearful, and neutral) were detected. The proposed attention-based deep learning model achieved an average test accuracy rate of 90%, which is a substantial improvement over established models. Hence, this emotion detection model has the potential to improve automated mental health monitoring.

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Section: Literature Reviewmentioning

confidence: 99%