A Crowdsourced Open-Source K azakh Speech Corpus and Initial Speech Recognition Baseline (2021)

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.

## introduction we present an open-source kazakh speech corpus (ksc) constructed to advance the development of speech and language processing applications for the kazakh language. kazakh is an agglutinative language with vowel harmony and belongs to the family of turkic languages. during the soviet period, the kazakh language was overwhelmed by the russian language, which caused a decline in kazakh language usage @xcite . in the 1990s, it was declared an official language of kazakhstan, and many initiatives were launched to increase the number of kazakh speakers. today, it is spoken by over 10 million people in kazakhstan and by over 3 million people in other countries foot_0 . by introducing the ksc, we aim to accelerate the penetration of the kazakh language into the internet of things (iot) technologies and to promote research in kazakh speech processing applications. although several kazakh speech corpora have been presented @xcite @xcite , there is no generally accepted common corpus. most of them are either publicly unavailable or contain an insufficient amount of data to train reliable models. especially, these databases are too small for building recent end-to-end models, which are extremely data hungry @xcite . consequently, different research groups usually conduct their experiments on internally collected data, which prevents the reproducibility and comparison of different approaches. to address the aforementioned limitations, we created the ksc, containing around 332 hours of transcribed audio. it was crowdsourced through the internet, where volunteers were asked to read sentences presented through a web browser. in total, we accepted over 153,000 utterances submitted from over 1,600 unique device ids. the recordings were first checked manually, and when a sufficient amount of data was collected, were partially checked automatically using a speech recognition system. to the best of our knowledge, the ksc is the largest open-source speech corpus in kazakh and is available for academic and commercial use upon request at this link foot_1 under the creative commons attribution 4.0 international license foot_2 . we expect that this database will be a valuable resource for research communities in both academia and industry. the primary application domains of the corpus are speech recognition, speech synthesis, and speaker recognition. to demonstrate the reliability of the database, we performed preliminary automatic speech recognition (asr) experiments, where promising and sufficient for practical usage results were achieved. we also provide a practical guide on the development of asr systems for the kazakh language by sharing the reproducible recipe and pretrained models foot_3 . the utilization of the database for speech synthesis and speaker recognition tasks is left for future work. the rest of the paper is organized as follows. section 2 provides a review of related works. section 3 presents the ksc database and explains the database construction procedures in detail. section 4 offer a presentation of the speech recognition experiment setup and obtained results. in section 5, the obtained results and challenges are discussed. lastly, section 6 concludes this paper and mentions potential future work. ## related works in the past few years, the interest in asr has surged by its new applications in smart devices, such as voice command, voice search, message dictation, and virtual assistants @xcite . in response to this technological shift, many speech corpora have been introduced for various languages. for example, @xcite released 1,000 hours of open-source mandarin read speech data to bridge the gap between academia and industry. the utterances were recorded using ios-based mobile phones and cover the following domains: voice command, smart home, autonomous driving, and so on. similarly, @xcite developed a 2,000-hour read speech corpus to help speech technology developers and researchers build speechrelated applications in singapore. several works have presented speech databases for the kazakh language. for example, @xcite developed a kazakh language corpus containing around 40 hours of transcribed read speech data recorded in a sound-proof studio. similarly, @xcite collected 76 hours of data using a professional recording booth which were further extended to 123 hours in @xcite . @xcite utilized 147 hours of bilingual kazakh-russian speech data to build code-switching asr systems. @xcite released 78 hours of transcribed kazakh speech data recorded by 96 students from china. the iarpa babel project has released a kazakh language pack foot_4 consisting of around 50 hours of conversational and 14 hours of scripted telephone speech. unfortunately, the aforementioned databases are either publicly unavailable or of an insufficient size to build robust kazakh asr systems. additionally, some of them are nonrepresentative-that is, they cover speakers from a narrow set of categories, such as the same region or age group. furthermore, since most of these databases have been collected in optimal lab settings, they might be ineffective for real-world applications. the emergence of crowdsourcing platforms and the growth in internet connectivity has motivated researchers to employ crowdsourcing for annotated corpora construction. different from the expert-based approaches, crowdsourcing tends to be cheaper and faster, though, additional measures should be taken to ensure the data quality @xcite @xcite . furthermore, crowdsourcing allows to gather a variety of dialects and accents from remote geographical locations and enables the participation of people with disabilities and of an advanced age, which otherwise would be impossible or too costly @xcite . inspired by this, we followed the best crowdsourcing practices to construct a large-scale and opensource speech corpus for the kazakh language, as described in the following sections. ## the ksc construction the ksc project was conducted with the approval of the institutional research ethics committee of nazarbayev university. each reader participated voluntarily and was informed of the data collection and use protocols through an online consent form. ## speech recognition experiment to demonstrate the utility and reliability of the ksc database, we conducted speech recognition experiments using both the traditional deep neural network-hidden markov model (dnn-hmm) and recently proposed end-to-end (e2e) architectures. we did not compare or perform thorough architecture searches for either dnn-hmm and e2e, since this falls outside the scope of this paper. ## conclusion in this work, we presented the ksc database containing around 332 hours of transcribed speech data. it was developed to advance kazakh speech processing applications, such as speech recognition, speech synthesis, and speaker recognition. we described the database construction procedures and discussed challenges that should be addressed in future work. the described methodologies might benefit other researchers planning to build a speech corpus for a low-resource language. the database is freely available for any purposes including research and commercial use. we also conducted preliminary speech recognition experiments using both traditional hybrid dnn-hmm and recently proposed e2e asr architectures. to ease the database usage and ensure the reproducibility of experiments, we split it into three non-overlapping sets (training, validation, and test) and released our espnet recipe. the detailed exploration of better asr settings, as well as the adaptation of the database to other applications, is left for future work.