Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database
Resumen: This work presents three novel speech recognition architectures evaluated on the Spanish RTVE2020 dataset, employed as the main evaluation set in the Albayzín S2T Transcription Challenge 2020. The main objective was to improve the performance of the systems previously submitted by the authors to the challenge, in which the primary system scored the second position. The novel systems are based on both DNN-HMM and E2E acoustic models, for which fully-and self-supervised learning methods were included. As a result, the new speech recognition engines clearly outper-formed the performance of the initial systems from the previous best WER of 19.27 to the new best of 17.60 achieved by the DNN-HMM based system. This work therefore describes an interesting benchmark of the latest acoustic models over a highly challenging dataset, and identifies the most optimal ones depending on the expected quality, the available resources and the required latency.
Idioma: Inglés
DOI: 10.3390/app12041889
Año: 2022
Publicado en: Applied Sciences (Switzerland) 12, 4 (2022), 1889 [16 pp.]
ISSN: 2076-3417

Factor impacto JCR: 2.7 (2022)
Categ. JCR: PHYSICS, APPLIED rank: 78 / 160 = 0.488 (2022) - Q2 - T2
Categ. JCR: ENGINEERING, MULTIDISCIPLINARY rank: 42 / 90 = 0.467 (2022) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 100 / 178 = 0.562 (2022) - Q3 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 208 / 343 = 0.606 (2022) - Q3 - T2

Factor impacto CITESCORE: 4.5 - Engineering (Q2) - Materials Science (Q2) - Chemical Engineering (Q2) - Computer Science (Q2) - Physics and Astronomy (Q2)

Factor impacto SCIMAGO: 0.492 - Fluid Flow and Transfer Processes (Q2) - Materials Science (miscellaneous) (Q2) - Engineering (miscellaneous) (Q2) - Instrumentation (Q2) - Process Chemistry and Technology (Q3) - Computer Science Applications (Q3)

Tipo y forma: Artículo (Versión definitiva)

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