Wav2vec2-based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering

With the rapid advancement in automatic speech recognition and
natural language understanding, a complementary field (paralin-
guistics) emerged, focusing on the non-verbal content of speech.
The ACM Multimedia 2022 Computational Paralinguistics Challenge introduced several exciting tasks of this field. In this work, we
focus on tackling two Sub-Challenges using modern, pre-trained
models called wav2vec2. Our experimental results demonstrated
that wav2vec2 is an excellent tool for detecting the emotions behind vocalisations and recognising different types of stutterings.
Albeit they achieve outstanding results on their own, our results
demonstrated that wav2vec2-based systems could be further improved by ensembling them with other models. Our best systems
outperformed the competition baselines by a considerable margin,
achieving an unweighted average recall of 44.0 (absolute improvement of 6.6% over baseline) on the Vocalisation Sub-Challenge and
62.1 (absolute improvement of 21.7% over baseline) on the Stuttering
Sub-Challenge.