A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English

Mussakhojayeva, Saida; Khassanov, Yerbolat; Varol, Hüseyin Atakan

doi:10.1007/978-3-030-87802-3_41

Cited by 12 publications

(5 citation statements)

References 23 publications

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“…To conduct training and testing of neural networks, datasets from the Institute of Smart Systems and Artificial Intelligence (ISSAI) of Nazarbayev University were used, namely the Kazakh speech corpus [22], Russian speech corpus [23], Turkish language corpus [24], and Uzbek language corpus [25]. One of the largest open datasets, the Common Voice Dataset [26], was also used, namely, the corpus of the Kyrgyz language, the corpus of the English language, and the corpus of the French language.…”

Section: Materials and Methods Of Researchmentioning

confidence: 99%

The dependence of the effectiveness of neural networks for recognizing human voice on language

Nurlankyzy,

Akhmediyarova,

Zhetpisbayeva

et al. 2024

EEJET

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This study examines the effectiveness of neural network architectures (multilayer perceptron MLP, convolutional neural network CNN, recurrent neural network RNN) for human voice recognition, with an emphasis on the Kazakh language. Problems related to language, the difference between speakers, and the influence of network architecture on recognition accuracy are considered. The methodology includes extensive training and testing, studying the accuracy of recognition in different languages, and different sets of data on speakers. Using a comparative analysis, this study evaluates the performance of three architectures trained exclusively in the Kazakh language. The testing included statements in Kazakhs and other languages, while the number of speakers varied to assess its impact on recognition accuracy. During the study, the results showed that CNN neural networks are more effective in recognizing human voice than RNN and MLP. Also, it was found that the CNN has a higher accuracy in recognizing the human voice in the Kazakh language, both for a small and for a large number of announcers. For example, for 20 speakers, the recognition error in Russian was 21.86 %, whereas in Kazakhs it was 10.6 %. A similar trend was observed for 80 speakers: 16.2 % Russians and 8.3 % Kazakhs. It can also be argued that learning one language does not guarantee high recognition accuracy in other languages. Therefore, the accuracy of human voice recognition by neural networks depends significantly on the language in which training is conducted. In addition, this study highlights the importance of different sets of speaker data to achieve optimal results. This knowledge is crucial for advancing the development of reliable human voice recognition systems that can accurately identify different human voices in different language contexts

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Section: Materials and Methods Of Researchmentioning

confidence: 99%

The dependence of the effectiveness of neural networks for recognizing human voice on language

Nurlankyzy,

Akhmediyarova,

Zhetpisbayeva

et al. 2024

EEJET

View full text Add to dashboard Cite

show abstract

“…A number of models [ 38 , 39 , 40 , 41 , 42 , 43 , 44 ] have been created for the speech recognition of the Kazakh language. The complexity with regard to Kazakh, its distinctive features, the scarcity of emotional speech datasets, and other factors make it difficult to develop a model for emotional speech detection in this language.…”

Section: Related Workmentioning

confidence: 99%

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

Amangeldy

Ukenova

Bekmanova

et al. 2023

Sensors

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This article is devoted to solving the problem of converting sign language into a consistent text with intonation markup for subsequent voice synthesis of sign phrases by speech with intonation. The paper proposes an improved method of continuous recognition of sign language, the results of which are transmitted to a natural language processor based on analyzers of morphology, syntax, and semantics of the Kazakh language, including morphological inflection and the construction of an intonation model of simple sentences. This approach has significant practical and social significance, as it can lead to the development of technologies that will help people with disabilities to communicate and improve their quality of life. As a result of the cross-validation of the model, we obtained an average test accuracy of 0.97 and an average val_accuracy of 0.90 for model evaluation. We also identified 20 sentence structures of the Kazakh language with their intonational model.

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“…Since then, research on Kazakh has been in the fast lane. For example, Mussakhojayeva et al [149] conducted a multilingual study of Kazakh, Russian, and English based on KSC. In 2022, KSC was further expanded and reached 1128 h [117].…”

Section: Ksc/ksc2mentioning

confidence: 99%

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

Maimaitiyiming

Nijat

et al. 2022

Applied Sciences

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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.

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A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English

Cited by 12 publications

References 23 publications

The dependence of the effectiveness of neural networks for recognizing human voice on language

The dependence of the effectiveness of neural networks for recognizing human voice on language

Continuous Sign Language Recognition and Its Translation into Intonation-Colored Speech

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

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