KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 5404–5411 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 5404 KazakhTTS2: Extending the Open-Source Kazakh TTS Corpus With More Data, Speakers, and Topics Saida Mussakhojayeva, Yerbolat Khassanov, Huseyin Atakan Varol Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Nur-Sultan, Kazakhstan {saida.mussakhojayeva, yerbolat.khassanov, ahvarol}@nu.edu.kz Abstract We present an expanded version of our previously released Kazakh text-to-speech (KazakhTTS) synthesis corpus. In the new KazakhTTS2 corpus, the overall size has increased from 93 hours to 271 hours, the number of speakers has risen from two to five (three females and two males), and the topic coverage has been diversified with the help of new sources, including a book and Wikipedia articles. This corpus is necessary for building high-quality TTS systems for Kazakh, a Central Asian agglutinative language from the Turkic family, which presents several linguistic challenges. We describe the corpus construction process and provide the details of the training and evaluation procedures for the TTS system. Our experimental results indicate that the constructed corpus is sufficient to build robust TTS models for real-world applications, with a subjective mean opinion score ranging from 3.6 to 4.2 for all the five speakers. We believe that our corpus will facilitate speech and language research for Kazakh and other Turkic languages, which are widely considered to be low-resource due to the limited availability of free linguistic data. The constructed corpus, code, and pretrained models are publicly available in our GitHub repository. Keywords: text-to-speech, TTS, speech synthesis, speech corpus, open-source, Kazakh, Turkic, agglutinative 1. Introduction Text-to-speech (TTS), also known as speech synthesis, is the automatic process of converting written text into speech (Taylor, 2009), which has wide application po- tential and a substantial social impact, including dig- ital assistants, improved accessibility for people with reading disabilities, speech and vision impairments, to name a few. For visually impaired people, in partic- ular, it enables voice-controlled access to Internet-of- things devices, on-demand access to books and web- sites, and access to other vocalized assistive technolo- gies. In turn, these enhance the overall quality of life, consumption of information, and access to knowledge. In addition, TTS can complement other important lan- guage and vision technologies, such as speech recog- nition (Tjandra et al., 2017), speech-to-speech transla- tion (Wahlster, 2013), face-to-face translation (Prajwal et al., 2019), and visual-to-sound (Zhou et al., 2018). Considering the aforementioned benefits, TTS is un- doubtedly an essential speech processing technology for any language. In recent years, TTS research has progressed remark- ably thanks to neural network-based architectures (Tan et al., 2021), regularly organized challenges (Black and Tokuda, 2005; Dunbar et al., 2019), and open-source datasets (Ito and Johnson, 2017; Zen et al., 2019; Shi et al., 2020). Especially, impressive results have been achieved for commercially viable languages, such as English and Mandarin. However, there is still a lack of research into the development of TTS technologies for low-resource languages. To address this problem in regard to Kazakh, …

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