Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Automatic Speech Recognition (ASR) systems have witnessed significant advancements in recent years, fueled by the development of deep neural networks and large-scale datasets. Multilingual end-to-end ASR, which aims to recognize speech in multiple languages using a unified framework, has gained substantial attention due to its potential applications in various domains. This article thoroughly analyses the design of deep neural frameworks for end-to-end, multilingual automatic continuous voice recognition. It offers an understanding of the latest techniques and possible directions for future research by examining the key components, challenges, and recent advancements within this domain through the lens of Wav2Vec. Wav2vec-U increases unsupervised recognition outcomes across a variety of languages, according to experiments, despite having a simpler conceptual framework.
"A Survey on Multilingual End-to-End Automatic Continuous Speech Recognition Using Deep Neural Network", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 8, page no.331 - 342, August-2023, Available :http://www.ijrti.org/papers/IJRTI2308055.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator