CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition

Abstract

Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association between state-of-the-art ASR models and the SER task has yet to be investigated. In this paper, we propose a novel channel and temporal-wise attention RNN (CTA-RNN) architecture based on the intermediate representations of pre-trained ASR models. Specifically, the embeddings of a large-scale pre-trained end-to-end ASR encoder contain both acoustic and linguistic information, as well as the ability to generalize to different speakers, making them well suited for downstream SER task. To further exploit the embeddings from different layers of the ASR encoder, we propose a novel CTA-RNN architecture to capture the emotional salient parts of embeddings in both the channel and temporal directions. We evaluate our approach on two popular benchmark datasets, IEMOCAP and MSP-IMPROV, using both within-corpus and cross-corpus settings. Experimental results show that our proposed method can achieve excellent performance in terms of accuracy and robustness.