Аңдатпа
We present a multilingual automatic speech recognition (ASR) system for Kazakh, Russian, and English designed for the trilingual community of Kazakhstan. Although prior research has shown that speech-based text entry can outperform conventional keyboard typing for human–computer interaction and interaction with large language models (LLMs), little is known about its performance and usability in low-resource multilingual contexts, particularly for Kazakh. To address this gap, we fine-tuned a Whisper-based model on additional Kazakh speech data, achieving a large reduction in Kazakh word error rate (WER) from 21.55% with the OpenAI baseline to 8.84%, while preserving competitive performance for Russian and English. We then conducted a user study with 38 participants from Nazarbayev University, who performed dictated reading and editing tasks in all three languages. We evaluated performance using WPM, CPM, WER, and CER, and assessed usability and cognitive effort using the System Usability Scale (SUS) and the Raw NASA Task Load Index (NASA-TLX). Participants reached high speech-based typing speeds without editing and moderate speeds with editing across all three languages. Importantly, there were no statistically significant differences between Kazakh, Russian, and English in error rates, cognitive load, or perceived usability. Users reported low cognitive load (NASA-TLX < 40) and consistently high usability (SUS > 80%), indicating that the interface is efficient, easy to use, and requires minimal mental effort. These results demonstrate that Kazakh-adapted Whisper enables accurate, usable, and low-effort multilingual ASR, and highlight the potential of speech-driven text entry systems for trilingual contexts such as Kazakhstan.