Development of integral model of speech recognition system for Uzbek language

Musaev, Muhammadjon; Khujayorov, Ilyos; Ochilov, Mannon

doi:10.1109/aict50176.2020.9368719

Cited by 11 publications

(5 citation statements)

References 25 publications

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“…LSTM tarmog'ining xotira blokiga misol 9-rasmda ko'rsatilgan. Rasmdagi t x t vaqt momentidagi kiruvchi vektor, t h -chiquvchi vektor sifatida qarash mumkin [11].…”

Section: -Rasm Takroriy Neyron Tarmoq Arxitekturasiunclassified

Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash

Xujayarov,

Ochilov,

Xolmatov

et al. 2024

Международный журнал теоретических и прикладных вопросов цифров

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Maqolada matn tilini aniqlashning sun’iy intellekt algoritmlariga asoslangan yondashuvlari muhokama qilinadi. Matn tilini aniqlash sun’iy intelletkning sinflashtirish masalasi bo‘lganligi sababli, maqolada mashinali o‘qitish va neyron tarmoq modellarining bir nechta sinflashtirish algoritmlari imkoniyatlari ko‘rib o‘tiladi. Ishda o‘zbek, inlgiz, rus, qoraqalpoq tillarini aniqlovchi model uchun o‘quv ma’lumotlar to‘plamini shakllantirish masalasi ko‘riladi. Shuningdek matn tilini aniqlashda foydalanilgan modellarning aniqlik ko‘rsatkichlari bo‘yicha qiyosiy tahlil amalga oshiriladi.

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“…LSTM tarmog'ining xotira blokiga misol 9-rasmda ko'rsatilgan. Rasmdagi t x t vaqt momentidagi kiruvchi vektor, t h -chiquvchi vektor sifatida qarash mumkin [11].…”

Section: -Rasm Takroriy Neyron Tarmoq Arxitekturasiunclassified

Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash

Xujayarov,

Ochilov,

Xolmatov

et al. 2024

Международный журнал теоретических и прикладных вопросов цифров

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“…For example, Musaev et al [23] developed an ASR system for geographical entities using a dataset consisting of 3,500 utterances. Similarly, the authors of [21] developed a read speech recognition system using 10 hours of transcribed audio. The works of [20] and [22] addressed spoken digit and voice command recognition systems under the limited vocabulary scenarios, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Previously, several works have addressed Uzbek speech recognition [21,23]. However, to the best of our knowledge, there has been no work presenting an open-source Uzbek speech corpus of high quality and sufficient size for training robust speech recognition systems.…”

Section: Introductionmentioning

confidence: 99%

USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments

Musaev¹,

Mussakhojayeva²,

Khujayorov³

et al. 2021

Preprint

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We present a freely available speech corpus for the Uzbek language and report preliminary automatic speech recognition (ASR) results using both the deep neural network hidden Markov model (DNN-HMM) and end-to-end (E2E) architectures. The Uzbek speech corpus (USC) comprises 958 different speakers with a total of 105 hours of transcribed audio recordings. To the best of our knowledge, this is the first open-source Uzbek speech corpus dedicated to the ASR task. To ensure high quality, the USC has been manually checked by native speakers. We first describe the design and development procedures of the USC, and then explain the conducted ASR experiments in detail. The experimental results demonstrate promising results for the applicability of the USC for ASR. Specifically, 18.1% and 17.4% word error rates were achieved on the validation and test sets, respectively. To enable experiment reproducibility, we share the USC dataset, pre-trained models, and training recipes in our GitHub repository 1 .

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“…Continuous speech recognition requires an LM to be built into an efficient system. An LM is essential for many applications in natural language processing (NLP), including but not limited to handwriting recognition [ 1 , 2 , 3 , 4 ], machine translation [ 5 ], speech recognition [ 6 , 7 ], integral model [ 8 ], phonemes [ 9 ], and vowels [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Development of Language Models for Continuous Uzbek Speech Recognition System

Mukhamadiyev

Mukhiddinov

Khujayorov

et al. 2023

Sensors

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Automatic speech recognition systems with a large vocabulary and other natural language processing applications cannot operate without a language model. Most studies on pre-trained language models have focused on more popular languages such as English, Chinese, and various European languages, but there is no publicly available Uzbek speech dataset. Therefore, language models of low-resource languages need to be studied and created. The objective of this study is to address this limitation by developing a low-resource language model for the Uzbek language and understanding linguistic occurrences. We proposed the Uzbek language model named UzLM by examining the performance of statistical and neural-network-based language models that account for the unique features of the Uzbek language. Our Uzbek-specific linguistic representation allows us to construct more robust UzLM, utilizing 80 million words from various sources while using the same or fewer training words, as applied in previous studies. Roughly sixty-eight thousand different words and 15 million sentences were collected for the creation of this corpus. The experimental results of our tests on the continuous recognition of Uzbek speech show that, compared with manual encoding, the use of neural-network-based language models reduced the character error rate to 5.26%.

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Development of integral model of speech recognition system for Uzbek language

Cited by 11 publications

References 25 publications

Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash

Sun’iy intellekt algoritmlari asosida matn tilini avtomatik aniqlash

USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments

Development of Language Models for Continuous Uzbek Speech Recognition System

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