Advanced LSTM: A Study About Better Time Dependency Modeling in Emotion Recognition

Fei Tao · Gang Liu

98 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Sentiment Analysis and Opinion Mining

Abstract

Long short-term memory (LSTM) is normally used in recurrent neural network (RNN) as basic recurrent unit. However, conventional LSTM assumes that the state at current time step depends on previous time step. This assumption constraints the time dependency modeling capability. In this study, we propose a new variation of LSTM, advanced LSTM (A-LSTM), for better temporal context modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based weighted pooling RNN can also complement the state-of-the-art emotion classification framework. This shows the advantage of A-LSTM.

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References

2008IEMOCAP: interactive emotional dyadic motion capture database 2015The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for Voice Research and Affective Computing 2017Automatic speech emotion recognition using recurrent neural networks with local attention 2015A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space 2017Jointly Predicting Arousal, Valence and Dominance with Multi-Task Learning

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2019Locally Confined Modality Fusion Network With a Global Perspective for Multimodal Human Affective Computing 2023New Trends in Emotion Recognition Using Image Analysis by Neural Networks, a Systematic Review 2020Multi-Fusion Residual Memory Network for Multimodal Human Sentiment Comprehension 2025Sparse temporal aware capsule network for robust speech emotion recognition 2024ESERNet: Learning spectrogram structure relationship for effective speech emotion recognition with swin transformer in classroom discourse analysis 2020A Lightweight Model Based on Separable Convolution for Speech Emotion Recognition 2022Multitask Learning From Augmented Auxiliary Data for Improving Speech Emotion Recognition

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