Curriculum Optimization for Low-Resource Speech Recognition

Kuznetsova, Anastasia; Kumar, Anurag; Fox, Jennifer Drexler; Tyers, Francis M.

doi:10.1109/icassp43922.2022.9746674

Cited by 4 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2019, the Common Voice database was published, which contains a small amount of Kyrgyz data. This public data aroused the interest of researchers, and since then, Kyrgyz has become the research object of extremely low-resource language speech recognition [151,152]. For example, Riviere et al [153] in 2020 verified the performance of transfer learning on these data.…”

Section: Ksc/ksc2mentioning

confidence: 99%

Automatic Speech Recognition for Uyghur, Kazakh, and Kyrgyz: An Overview

Maimaitiyiming

Nijat

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

With the emergence of deep learning, the performance of automatic speech recognition (ASR) systems has remarkably improved. Especially for resource-rich languages such as English and Chinese, commercial usage has been made feasible in a wide range of applications. However, most languages are low-resource languages, presenting three main difficulties for the development of ASR systems: (1) the scarcity of the data; (2) the uncertainty in the writing and pronunciation; (3) the individuality of each language. Uyghur, Kazakh, and Kyrgyz as examples are all low-resource languages, involving clear geographical variation in their pronunciation, and each language possesses its own unique acoustic properties and phonological rules. On the other hand, they all belong to the Altaic language family of the Altaic branch, so they share many commonalities. This paper presents an overview of speech recognition techniques developed for Uyghur, Kazakh, and Kyrgyz, with the purposes of (1) highlighting the techniques that are specifically effective for each language and generally effective for all of them and (2) discovering the important factors in promoting the speech recognition research of low-resource languages, by a comparative study of the development path of these three neighboring languages.

show abstract

Section: Ksc/ksc2mentioning

confidence: 99%