Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools

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Publisher: SMPTE
Doc Type: Journal Article
Content Type: Original Research
Abbreviated Title: SMPTE Motion Imaging Jour.
Volume: 133, No. 2, pp. 48–57
Abstract: This research offers an in-depth review of current Automatic Speech Recognition (ASR) methods and their significant impact on media production, with a focus on the transformer model's self-attention mechanism for understanding sequential relationships. It compares accuracy and performance of top ASR models like Meta's Multilingual Machine Speech, OpenAI's Whisper, and Google's Universal Speech Model along with services from Microsoft Azure, Amazon Web Services, and Google Cloud Platform. The study examines key ASR aspects, including voice activity detection, language identification, and multilanguage support, and evaluates their accuracy metrics. Challenges such as limited data for certain languages and complexities in linguistic nuances are highlighted. Additionally, the paper discusses ASR's role in media production, from creating time-based captions to transforming editing techniques. By analyzing the ASR process from audio preprocessing to post-processing, the research bridges academic and practical perspectives, enabling media producers to utilize advanced ASR technologies effectively.
Publication Date: 2024-04-01
DOI: 10.5594/JMI.2024/IPYX8877
ISSN: Print: 1545-0279 | Electronic: 2160-2492
Link: https://doi.org/10.5594/JMI.2024/IPYX8877
Author(s): Randy Fayan
Zahra Montajabi
Rob Gonsalves
Keyword(s): Artificial Intelligence, Machine Learning, Automatic Speech Recognition
Copyright: © 2024 SMPTE

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Randy Fayan, Zahra Montajabi, and Rob Gonsalves; Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools, MIJ 2024, Volume 133, Number 2 (pp. 48 to 57); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/IPYX8877

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Randy Fayan, Zahra Montajabi, and Rob Gonsalves; Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools, MIJ 2024, Volume 133, Number 2 (pp. 48 to 57); SMPTE, 2024. Available at https://doi.org/10.5594/JMI.2024/IPYX8877

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<span class="citation">Randy Fayan, Zahra Montajabi, and Rob Gonsalves; <cite>Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools</cite>, MIJ 2024, Volume 133, Number 2 (pp. 48 to 57); SMPTE, 2024. Available at <a href="https://doi.org/10.5594/JMI.2024/IPYX8877" target="_blank" rel="noopener">https://doi.org/10.5594/JMI.2024/IPYX8877</a></span>

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Randy Fayan, Zahra Montajabi, and Rob Gonsalves; Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools, MIJ 2024, Volume 133, Number 2 (pp. 48 to 57); SMPTE, 2024
doi: 10.5594/JMI.2024/IPYX8877
url: https://doi.org/10.5594/JMI.2024/IPYX8877

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<li>
Randy Fayan, Zahra Montajabi, and Rob Gonsalves; <cite id="bib-10-5594-jmi-2024-ipyx8877__2024-smptemijapril2024_18-18">Automatic Speech Recognition with Machine Learning: Techniques and Evaluation of Current Tools</cite>, MIJ 2024, Volume 133, Number 2 (pp. 48 to 57); SMPTE, 2024
<span class="doi">10.5594/JMI.2024/IPYX8877</span>
</li>