Computer Science > Computation and Language
[Submitted on 9 Jun 2021 (v1), last revised 20 Mar 2022 (this version, v2)]
Title:Unsupervised Automatic Speech Recognition: A Review
View PDFAbstract:Automatic Speech Recognition (ASR) systems can be trained to achieve remarkable performance given large amounts of manually transcribed speech, but large labeled data sets can be difficult or expensive to acquire for all languages of interest. In this paper, we review the research literature to identify models and ideas that could lead to fully unsupervised ASR, including unsupervised segmentation of the speech signal, unsupervised mapping from speech segments to text, and semi-supervised models with nominal amounts of labeled examples. The objective of the study is to identify the limitations of what can be learned from speech data alone and to understand the minimum requirements for speech recognition. Identifying these limitations would help optimize the resources and efforts in ASR development for low-resource languages.
Submission history
From: Hanan Aldarmaki [view email][v1] Wed, 9 Jun 2021 08:33:20 UTC (111 KB)
[v2] Sun, 20 Mar 2022 09:46:53 UTC (242 KB)
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