Topic Identification for Spontaneous Speech: Enriching Audio Features with Embedded Linguistic Information

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

Traditional topic identification solutions from audio rely on an automatic speech recognition system (ASR) to produce transcripts used as input to a text-based model. These approaches work well in high-resource scenarios, where there are sufficient data to train both components of the pipeline. However, in low-resource situations, the ASR system, even if available, produces low-quality transcripts, leading to a bad text-based classifier. Moreover, spontaneous speech containing hesitations can further degrade the performance of the ASR model. In this paper, we investigate alternatives to the standard text-only solutions by comparing audio-only and hybrid techniques of jointly utilising text and audio features. The models evaluated on spontaneous Finnish speech demonstrate that purely audio-based solutions are a viable option when ASR components are not available, while the hybrid multi-modal solutions achieve the best results.
AlkuperäiskieliEnglanti
Otsikko2023 31st European Signal Processing Conference (EUSIPCO)
KustantajaIEEE
Sivut396-400
Sivumäärä5
ISBN (elektroninen)978-9-4645-9360-0
ISBN (painettu)979-8-3503-2811-0
DOI - pysyväislinkit
TilaJulkaistu - 4 syysk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaEuropean Signal Processing Conference - Helsinki, Suomi
Kesto: 4 syysk. 20238 syysk. 2023
Konferenssinumero: 31
https://eusipco2023.org/

Julkaisusarja

NimiEuropean Signal Processing Conference
ISSN (elektroninen)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
LyhennettäEUSIPCO
Maa/AlueSuomi
KaupunkiHelsinki
Ajanjakso04/09/202308/09/2023
www-osoite

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