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

arXiv: https://arxiv.org/abs/2201.05771 · HTML: https://mlsanity.com/paper/2201.05771/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 29 references that are in the map.
- First posted on arXiv 15 Jan 2022.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [A Survey on Neural Speech Synthesis](https://mlsanity.com/paper/2106.15561/) (2021)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [Visual to Sound: Generating Natural Sound for Videos in the Wild](https://mlsanity.com/paper/1712.01393/) (2017)
- [Towards Automatic Face-to-Face Translation](https://mlsanity.com/paper/2003.00418/) (2020)
- [ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit](https://mlsanity.com/paper/1910.10909/) (2019)
- [Listening while Speaking: Speech Chain by Deep Learning](https://mlsanity.com/paper/1707.04879/) (2017)
- [The Zero Resource Speech Challenge 2019: TTS without T](https://mlsanity.com/paper/1904.11469/) (2019)
- [End-to-end Text-to-speech for Low-resource Languages by Cross-Lingual Transfer Learning](https://mlsanity.com/paper/1904.06508/) (2019)
- [KazNERD: Kazakh Named Entity Recognition Dataset](https://mlsanity.com/paper/2111.13419/) (2021)
- [A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline](https://mlsanity.com/paper/2009.10334/) (2020)
- [KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset](https://mlsanity.com/paper/2104.08459/) (2021)
- [USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments](https://mlsanity.com/paper/2107.14419/) (2021)
- [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://mlsanity.com/paper/2108.01280/) (2021)

## Built on by (most cited first, 10 of 10)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [OmniVoice: Towards Omnilingual Zero-Shot Text-to-Speech with Diffusion Language Models](https://mlsanity.com/paper/2604.00688/) (2026)
- [Recent Advancements and Challenges of Turkic Central Asian Language Processing](https://mlsanity.com/paper/2407.05006/) (2024)
- [KazSAnDRA: Kazakh Sentiment Analysis Dataset of Reviews and Attitudes](https://mlsanity.com/paper/2403.19335/) (2024)
- [KazParC: Kazakh Parallel Corpus for Machine Translation](https://mlsanity.com/paper/2403.19399/) (2024)
- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)
- [Multilingual Text-to-Speech Synthesis for Turkic Languages Using Transliteration](https://mlsanity.com/paper/2305.15749/) (2023)
- [TurkicNLP: An NLP Toolkit for Turkic Languages](https://mlsanity.com/paper/2602.19174/) (2026)
- [Lost in Speech: Trilingual Spoken Hallucination Detection Across Audio and Transcripts](https://mlsanity.com/paper/2608.24707/) (2026)
- [TRILOGUE: A Trilingual Spoken Dialogue Fact-Checking Benchmark with Evidence and Paired Audio](https://mlsanity.com/paper/2609.04452/) (2026)

## Abstract (arXiv)

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.