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Title: Speech emotion recognition using WaveNet
Authors: Nurul Sabrina Mohammed Riduwan
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Nurul Sabrina Mohammed Riduwan (2022). Speech emotion recognition using WaveNet. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: SCSE21-0421
Abstract: Speech emotion recognition is known to be a challenging and complex task for machine learning models. Two challenges that are faced when doing speech emotion recognition are 1) human emotions are hard to distinguished and 2) detection of emotion could only be captured at specific moments in an utterance. Hereby, this paper proposes a Speech Emotion Recognition (SER) architecture inspired by WaveNet architecture. This architecture does not rely neither on tedious pre-processing nor the recurrent layers. The novelty of our approach uses both speech waveforms and audio features as inputs, usage on casual dilated convolutions for capturing temporal dependencies and the use of self-attention mechanism. Self-attention permit inputs to interact with each other to pay close attention on the valuable parts of the input to learn the connection between them. We illustrate improved performances SER with our model on EMO-DB datasets over the existing base-line models. Index Term: speech emotion recognition, self-attention, deep learning, computational paralinguistics
Schools: School of Computer Science and Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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