Attention-LSTM-Attention Model for Speech Emotion Recognition and Analysis of IEMOCAP Database

Yeonguk Yu · Yoon-Joong Kim

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

Abstract

We propose a speech-emotion recognition (SER) model with an “attention-long Long Short-Term Memory (LSTM)-attention” component to combine IS09, a commonly used feature for SER, and mel spectrogram, and we analyze the reliability problem of the interactive emotional dyadic motion capture (IEMOCAP) database. The attention mechanism of the model focuses on emotion-related elements of the IS09 and mel spectrogram feature and the emotion-related duration from the time of the feature. Thus, the model extracts emotion information from a given speech signal. The proposed model for the baseline study achieved a weighted accuracy (WA) of 68% for the improvised dataset of IEMOCAP. However, the WA of the proposed model of the main study and modified models could not achieve more than 68% in the improvised dataset. This is because of the reliability limit of the IEMOCAP dataset. A more reliable dataset is required for a more accurate evaluation of the model’s performance. Therefore, in this study, we reconstructed a more reliable dataset based on the labeling results provided by IEMOCAP. The experimental results of the model for the more reliable dataset confirmed a WA of 73%.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction 2018Speech emotion recognition using deep 1D & 2D CNN LSTM networks 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2017Automatic speech emotion recognition using recurrent neural networks with local attention 2012Emotion recognition from speech: a review 20183-D Convolutional Recurrent Neural Networks With Attention Model for Speech Emotion Recognition 2015High-level feature representation using recurrent neural network for speech emotion recognition

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2022Speech emotion recognition based on convolutional neural network with attention-based bidirectional long short-term memory network and multi-task learning 2022Emotional Speech Recognition Using Deep Neural Networks 2021Spatiotemporal and frequential cascaded attention networks for speech emotion recognition 2023Speech Emotion Recognition Using Convolution Neural Networks and Multi-Head Convolutional Transformer

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