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Mobile devices are transforming the way people interact with computers, andspeech interfaces to applications are ever more important. Automatic SpeechRecognition systems recently published are very accurate, but often requirepowerful machinery (specialised Graphical Processing Units) for inference,which makes them impractical to run on commodity devices, especially instreaming mode. Impressed by the accuracy of, but dissatisfied with theinference times of the baseline Kazakh ASR model of (Khassanov et al.,2021)when not using a GPU, we trained a new baseline acoustic model (on the samedataset as the aforementioned paper) and three language models for use with theCoqui STT framework. Results look promising, but further epochs of training andparameter sweeping or, alternatively, limiting the vocabulary that the ASRsystem must support, is needed to reach a production-level accuracy.

A baseline model for computationally inexpensive speech recognition for Kazakh using the Coqui STT framework