Towards Discriminative Representation Learning for Speech Emotion Recognition

Runnan Li · Zhiyong Wu · Jia Jia · Yaohua Bu · Sheng Zhao · Helen M. L. Meng

47 citations0/0 files storedInternational Joint Conference on Artificial Intelligence

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

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

Abstract

In intelligent speech interaction, automatic speech emotion recognition (SER) plays an important role in understanding user intention. While sentimental speech has different speaker characteristics but similar acoustic attributes, one vital challenge in SER is how to learn robust and discriminative representations for emotion inferring. In this paper, inspired by human emotion perception, we propose a novel representation learning component (RLC) for SER system, which is constructed with Multi-head Self-attention and Global Context-aware Attention Long Short-Term Memory Recurrent Neutral Network (GCA-LSTM). With the ability of Multi-head Self-attention mechanism in modeling the element-wise correlative dependencies, RLC can exploit the common patterns of sentimental speech features to enhance emotion-salient information importing in representation learning. By employing GCA-LSTM, RLC can selectively focus on emotion-salient factors with the consideration of entire utterance context, and gradually produce discriminative representation for emotion inferring. Experiments on public emotional benchmark database IEMOCAP and a tremendous realistic interaction database demonstrate the outperformance of the proposed SER framework, with 6.6% to 26.7% relative improvement on unweighted accuracy compared to state-of-the-art techniques.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 47 citations exist upstream.

References

2008IEMOCAP: interactive emotional dyadic motion capture database 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 2015High-level feature representation using recurrent neural network for speech emotion recognition 2011Deep neural networks for acoustic emotion recognition: Raising the benchmarks 2015A Multi-Task Learning Framework for Emotion Recognition Using 2D Continuous Space 2012Ensemble of SVM trees for multimodal emotion recognition

Cited by

2022A Feature-Fused Convolutional Neural Network for Emotion Recognition From Multichannel EEG Signals 2021DECN: Dialogical emotion correction network for conversational emotion recognition 2020Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition 2022Speaker-Guided Encoder-Decoder Framework for Emotion Recognition in Conversation 2024Survey of deep emotion recognition in dynamic data using facial, speech and textual cues 2022

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.