Papers by Yerbolat Khassanov
KazNERD: Kazakh Named Entity Recognition Dataset (2022.lrec-1)
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| Challenge: | Named entity recognition (NER) is a subtask of information extraction aimed at identifying named entities (NEs) in semi-or unstructured text and classifying them into pre-specified types. |
| Approach: | They present a dataset for Kazakh named entity recognition using an annotation scheme and guidelines for annotation. |
| Outcome: | The dataset contains 112,702 sentences and 136,333 annotations for 25 entity classes. |
KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics (2022.lrec-1)
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| Challenge: | Text-to-speech (TTS) is a process of converting written text into speech. |
| Approach: | They present an expanded version of their text-to-speech corpus for Kazakh . they propose to use the corpus to build high-quality TTS systems for the language . |
| Outcome: | The constructed corpus is sufficient to build robust TTS models for Kazakh and other Turkic languages, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. |
A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline (2021.eacl-main)
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Yerbolat Khassanov, Saida Mussakhojayeva, Almas Mirzakhmetov, Alen Adiyev, Mukhamet Nurpeiissov, Huseyin Atakan Varol
| Challenge: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |
| Approach: | They propose to build an open-source Kazakh speech corpus for the Kazakh language that contains over 153,000 transcribed audio . they describe the data collection and preprocessing procedures followed by a description of the database specifications. |
| Outcome: | The Kazakh speech corpus contains over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. |