Towards A Common Speech Analysis Engine

• Published in: ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 8162 - 8166
• Main Authors: Aronowitz, Hagai, Gat, Itai, Morais, Edmilson, Zhu, Weizhong, Hoory, Ron
• Format: Conference Proceeding
• Language: English
• Published: IEEE 23.05.2022
• Subjects: Emotion recognition; language identification; self-supervised speech representations; Signal processing; Speech analysis; Speech processing; Technological innovation; Training; Training data
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP43922.2022.9747756

Recent innovations in self-supervised representation learning have led to remarkable advances in natural language processing. That said, in the speech processing domain, self-supervised representation learning-based systems are not yet considered state-of-the-art.We propose leveraging recent advances in self-supervised-based speech processing to create a common speech analysis engine. Such an engine should be able to handle multiple speech processing tasks, using a single architecture, to obtain state-of-the-art accuracy. The engine must also enable support for new tasks with small training datasets. Beyond that, a common engine should be capable of supporting distributed training with client in-house private data.We present the architecture for a common speech analysis engine based on the HuBERT self-supervised speech representation. Based on experiments, we report our results for language identification and emotion recognition on the standard evaluations NIST-LRE 07 and IEMOCAP. Our results surpass the state-of-the-art performance reported so far on these tasks.We also analyzed our engine on the emotion recognition task using reduced amounts of training data and show how to achieve improved results.