Formant set as a main parameter for recognizing vowels of the Uzbek language

Abdullaeva, Malika; Khujayorov, Ilyos; Ochilov, Mannon

doi:10.1109/icisct52966.2021.9670268

Cited by 6 publications

(2 citation statements)

References 7 publications

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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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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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“…RNN va LSTM lar afzalliklarini birlashtirgan holda ketmaket modellashtirishni amalga oshirish uchun biz avvalgi uzoq muddatli kontekstli bog'liqliklardan, shuningdek keyingi vaqt qadamlaridan foydadanishimiz mumkin. Bu jarayon 10rasmdagi 3 ta t-1, t, t+1 vaqtli qadamlar uchun kengaytirilgan BLSTM modelida ko'rsatilgan [13].…”

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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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