Аннотация
This article presents a comprehensive study of automatic speech recognition (ASR) for Kazakh children's speech, with a particular focus on the impact of signal preprocessing techniques in a low-resource linguistic environment. Although commercial ASR systems such as Whisper and DeepSpeech have shown recent progress, their accuracy on children's speech remains limited. We propose a robust ASR pipeline that incorporates Wiener filtering, denoising autoencoders, MFCC/log-Mel features, and transformer-based models (Whisper, Conformer). A novel Kazakh child speech corpus was collected from children aged 2 to 9 via Telegram bots, spontaneous recordings, and curated phrases. We evaluated four neural ASR models under noisy and clean conditions. Our experiments demonstrate that preprocessing significantly improves performance: Whisper achieved WER = 15.4%, CER = 12.3%, Accuracy = 75.2%, BLEU = 0.90. The proposed pipeline is effective for improving ASR accuracy for spontaneous and emotionally variable child speech in low-resource environments.