• Medientyp: Sonstige Veröffentlichung; E-Artikel; Elektronischer Konferenzbericht
  • Titel: Competitive Evaluation of Commercially Available Speech Recognizers in Multiple Languages
  • Beteiligte: Sloane, Zachary [Verfasser:in]; Burger, Susanne [Verfasser:in]; Yang, Jie [Verfasser:in]
  • Erschienen: Association for Computational Linguistics, 2024-01-03
  • Sprache: Englisch
  • DOI: https://doi.org/10.5445/IR/1000166410
  • Schlagwörter: DATA processing & computer science
  • Entstehung:
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  • Beschreibung: Recent improvements in speech recognition technology have resulted in products that can now demonstrate commercial value in a variety of applications. Many vendors are marketing products which combine ASR applications including continuous dictation, command-and-control interfaces, and transcription of recorded speech at an accuracy of 98%. In this study, we measured the accuracy of certain commercially available desktop speech recognition engines in multiple languages. Using word error rate as a benchmark, this work compares recognition accuracy across eight languages and the products of three manufacturers. Results show that two systems performed almost the same while a third system recognized at lower accuracy, although none of the systems reached the claimed accuracy. Read speech was recognized better than spontaneous speech. The systems for US-English, Japanese and Spanish showed higher accuracy than the systems for UK-English, German, French and Chinese.
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