Continuous Speech Recognition of Kazakh Language

Mamyrbayev, Оrken; Turdalyuly, Mussa; Mekebayev, Nurbapa; Mukhsina, Kuralay; Keylan, Alimukhan; BabaAli, Bagher; Nabieva, Gulnaz; Duisenbayeva, Aigerim; Akhmetov, Bekturgan

doi:10.1051/itmconf/20192401012

Cited by 12 publications

(5 citation statements)

References 2 publications

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“…Text files have been normalized by removing all the unnecessary characters and representing in lower case. The overall duration of our speech corpus, slightly inferior compared to corpuses that has been described here [25,26], where they have biggest speech corpus for Kazakh language. They have collected around 30 hours of data with 200 different speakers with different genders and ages.…”

Section: Datasets and Preprocessingmentioning

confidence: 83%

Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Amirgaliyev¹

2020

IJATCSE

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Development of an automatic speech recognition system for the Kazakh language is a challenging task due to the lack of audio data and specificity and complexity of the language itself. In this paper, we propose a new method which gets a pre-trained model of the russian language and uses the weight values of the pre-trained model in the proposed neural network. The main reason for choosing the Russian language model is that the pronunciation of the Kazakh and Russian languages is very similar in many respects, because they account for 78% of the total letters and there is a rather large corpus of the Russian speech dataset. The dataset of Kazakh speech with transcriptions was formed by the university's faculty. In general, 50 native speakers were involved who generated about 400 sentences. A special technology has been created for the automatic expansion of the database. The data was extracted from well-known Kazakh books such as "Abai zholy", "Kara sozder", etc.

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Section: Datasets and Preprocessingmentioning

confidence: 83%

Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Amirgaliyev¹

2020

IJATCSE

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“…In 2016, Abilhayer et al [141] constructed a continuous speech recognition system based on the GMM-HMM. Since 2019, the Institute of Information and Computational Technology and al-Farabi Kazakh National University published several papers on Kazakh speech recognition and verified the DNN-HMM system [142,143], BLSTM-CTC end-to-end system [144,145], and Transformer CTC/attention system [146] with their private data. At the same time, Beibut et al [147] constructed an LSTM-CTC end-to-end Kazakh ASR system based on transfer learning.…”

Section: Ksc/ksc2mentioning

confidence: 96%

“…Early databases generally contained reading speech recorded by microphones in an office environment (even high-quality speech recorded in a sound-proof studio [142,143]). Speech data recorded in this way are characterized by clear pronunciation, small noise interference, and a single channel.…”

Section: The Diversity Of Data Sourcesmentioning

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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“…In this work, we use such CRF-based NER system as one baseline and make comparison to our deep learning models. Recently, deep learning models including biLSTM have obtained a significant success on various natural languages processing tasks, such as POS tagging [28,13,26,25], NER [4,10], machine translation [2,8], word segmentation [10] and on other fields like speech recognition [15,15,7,16,1]. As the state-of-the-art of NER, in the study [12], the authors have explored various neural architectures for NER including the language independent character-based biLSTM-CRF models.…”

Section: Related Workmentioning

confidence: 99%

Neural Named Entity Recognition for Kazakh

Tolegen¹,

Toleu²,

Mamyrbayev³

et al. 2020

Preprint

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We present several neural networks to address the task of named entity recognition for morphologically complex languages (MCL). Kazakh is a morphologically complex language in which each root/stem can produce hundreds or thousands of variant word forms. This nature of the language could lead to a serious data sparsity problem, which may prevent the deep learning models from being well trained for underresourced MCLs. In order to model the MCLs' words effectively, we introduce root and entity tag embedding plus tensor layer to the neural networks. The effects of those are significant for improving NER model performance of MCLs. The proposed models outperform state-of-the-art including character-based approaches, and can be potentially applied to other morphologically complex languages.

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Continuous Speech Recognition of Kazakh Language

Cited by 12 publications

References 2 publications

Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

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

Neural Named Entity Recognition for Kazakh

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