Emotion Recognition from Bengali Speech using RNN Modulation-based Categorization

H. M Mahmudul Hasan · Md. Adnanul Islam

31 citations0/0 files storedInternational Conference on Smart Systems and Inventive Technology (ICSSIT)

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

Emotion recognition is one of the most challenging tasks in the field of speech recognition and processing. Besides, it is deemed a significant branch of Human-Computer Interaction (HCI). Although there exist several techniques related to speech emotion recognition (SER), in this study, machine learning approaches for feature extraction is used. To be precise, the Mel-frequency Cepstrum Coefficient (MFCC) and Modulation Spectral (MS) is implemented to extract the relevant features from Bengali speech. Our study focuses on recognizing several states of emotion such as joy, sadness, anger, surprise, fear, and disgust from Bengali speech by exploring an emotion recognition model. Besides, in this study, a novel Bengali speech dataset is developed and used since no proper study, specifically for Bengali, on this topic is available in the literature yet. In this study, the recurrent neural network (RNN) technology is used to categorize the human voice (speech) into six particular states of emotion. Finally, our study shows that while the RNN classifier model achieves 47.66% accuracy with our Bengali speech dataset, achieved 51.33% accuracy when the dataset is first categorized using the RNN modulation technique.

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References

2018The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS): A dynamic, multimodal set of facial and vocal expressions in North American English 2017Automatic speech emotion recognition using recurrent neural networks with local attention 2019Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers 2010Automatic speech emotion recognition using modulation spectral features 2018Databases, features and classifiers for speech emotion recognition: a review 2019Automatic Speech Emotion Recognition Using Machine Learning 2018Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languages 2015Towards a Small Set of Robust Acoustic Features for Emotion Recognition: Challenges

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