A Crowdsourced Open-Source Kazakh Speech Corpus and Initial Speech Recognition Baseline

TL;DR

This paper introduces the Kazakh Speech Corpus, a large open-source dataset for Kazakh speech processing, and provides initial speech recognition benchmarks demonstrating its quality and potential for advancing low-resource language technologies.

Contribution

It presents the largest publicly available Kazakh speech corpus with detailed data collection, quality assurance, and initial speech recognition results, facilitating future research in Kazakh language processing.

Findings

Character error rate of 2.8% on test set

Word error rate of 8.7% on test set

Released an ESPnet recipe for speech recognition

Abstract

We present an open-source speech corpus for the Kazakh language. The Kazakh speech corpus (KSC) contains around 332 hours of transcribed audio comprising over 153,000 utterances spoken by participants from different regions and age groups, as well as both genders. It was carefully inspected by native Kazakh speakers to ensure high quality. The KSC is the largest publicly available database developed to advance various Kazakh speech and language processing applications. In this paper, we first describe the data collection and preprocessing procedures followed by a description of the database specifications. We also share our experience and challenges faced during the database construction, which might benefit other researchers planning to build a speech corpus for a low-resource language. To demonstrate the reliability of the database, we performed preliminary speech recognition experiments. The experimental results imply that the quality of audio and transcripts is promising (2.8% character error rate and 8.7% word error rate on the test set). To enable experiment reproducibility and ease the corpus usage, we also released an ESPnet recipe for our speech recognition models.