Wav2vec2-based Paralinguistic Systems to Recognise Vocalised Emotions and Stuttering

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

99 Lataukset (Pure)

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the 30th ACM International Conference on Multimedia
KustantajaACM
Sivumäärä4
ISBN (elektroninen)978-1-4503-9203-7
DOI - pysyväislinkit
TilaJulkaistu - lokak. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaACM International Conference on Multimedia - Lisboa, Portugali
Kesto: 10 lokak. 202214 lokak. 2022
Konferenssinumero: 30

Conference

ConferenceACM International Conference on Multimedia
LyhennettäMM
Maa/AluePortugali
KaupunkiLisboa
Ajanjakso10/10/202214/10/2022

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