Named Entity Recognition from Kazakh Speech

This paper addresses the challenges of Named Entity Recognition (NER) in Kazakh speech, a critical task in Natural Language Processing (NLP). The integration of Automatic Speech Recognition (ASR) and NER technologies is explored to improve recognition accuracy. We evaluate two ASR models, Wav2vec2.0 and Whisper, fine-tuning them on the Kazakh Speech Corpus 2 (KSC2) dataset. Our study demonstrates that fine-tuning pre-trained mod-els can enhance performance, with end-to-end systems outper-forming traditional methods in speech-based NER tasks. The Transformer-based Wav2vec2.0 model showed superior NER accuracy, suggesting its potential for improving NER in low-resource languages like Kazakh.