Improving Speech Emotion Recognition with Unsupervised Representation Learning on Unlabeled Speech

Michael Neumann · Ngoc Thang Vu

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

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

Abstract

In this paper we present our findings on how representation learning on large unlabeled speech corpora can be beneficially utilized for speech emotion recognition (SER). Prior work on representation learning for SER mostly focused on the relatively small emotional speech datasets without making use of additional unlabeled speech data. We show that integrating representations learnt by an unsupervised autoencoder into a CNN-based emotion classifier improves the recognition accuracy. To gain insights about what those models learn, we analyze visualizations of the different representations using t-distributed neighbor embeddings (t-SNE). We evaluate our approach on IEMOCAP and MSP-IMPROV by means of within- and cross-corpus testing.

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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 2016Adieu features? End-to-end speech emotion recognition using a deep convolutional recurrent network 2018Speech emotion recognition 2016MSP-IMPROV: An Acted Corpus of Dyadic Interactions to Study Emotion Perception 2009Acoustic emotion recognition: A benchmark comparison of performances 2017Attentive Convolutional Neural Network Based Speech Emotion Recognition: A Study on the Impact of Input Features, Signal Length, and Acted Speech 2015A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space

Cited by

2023Affective Computing: Recent Advances, Challenges, and Future Trends 2023Speech emotion recognition using machine learning — A systematic review 2020Impact of Feature Selection Algorithm on Speech Emotion Recognition Using Deep Convolutional Neural Network 2020Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition 2021

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