Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems

As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems (ASR), and we benchmark three of them, namely the IBM Watson,...

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Autores principales: Filippidou, Foteini, Moussiades, Lefteris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256403/
http://dx.doi.org/10.1007/978-3-030-49161-1_7
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author Filippidou, Foteini
Moussiades, Lefteris
author_facet Filippidou, Foteini
Moussiades, Lefteris
author_sort Filippidou, Foteini
collection PubMed
description As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems (ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google’s automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data.
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spelling pubmed-72564032020-05-29 Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems Filippidou, Foteini Moussiades, Lefteris Artificial Intelligence Applications and Innovations Article As the requirements for automatic speech recognition are continually increasing, the demand for accuracy and efficiency is also of particular interest. In this paper, we present most of the well-known Automated Speech Recognition systems (ASR), and we benchmark three of them, namely the IBM Watson, Google, and Wit, using the WER, Hper, and Rper error metrics. The experimental results show that Google’s automatic speech recognition performs better among the three systems. We intend to extend the benchmarking both to include most of the available Automated Speech Recognition systems and increase our test data. 2020-05-06 /pmc/articles/PMC7256403/ http://dx.doi.org/10.1007/978-3-030-49161-1_7 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Filippidou, Foteini
Moussiades, Lefteris
Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title_full Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title_fullStr Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title_full_unstemmed Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title_short Α Benchmarking of IBM, Google and Wit Automatic Speech Recognition Systems
title_sort α benchmarking of ibm, google and wit automatic speech recognition systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256403/
http://dx.doi.org/10.1007/978-3-030-49161-1_7
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