This paper focuses on the implementation of the Whisper architecture to create an automatic speech recognition (ASR) system optimized for the Turkish language, which is considered a low-resource language in terms of speech recognition technologies. Whisper is a transformer-based model known for its high performance across numerous languages. However, its performance in Turkish, a language with unique linguistic features and limited labeled data, has yet to be fully explored. To address this, we conducted a series of experiments using five different Turkish speech datasets to assess the model’s baseline performance. Initial evaluations revealed a range of word error rates (WERs) between 4.3% and 14.2%, reflecting the challenges posed by Turkish. To improve these results, we applied the low-rank adaptation (LoRA) technique, which is designed to fine-tune large-scale models efficiently by introducing a reduced set of trainable parameters. After fine-tuning, significant performance improvements were observed, with WER reductions of up to 52.38%. This study demonstrates that fine-tuned Whisper models can be successfully adapted for Turkish, resulting in a robust and accurate end-to-end ASR system. This research highlights the applicability of Whisper in low-resource languages and provides insights into the challenges of and strategies for improving speech recognition performance in Turkish.

Makine öğrenmesinde, etiketli verinin yetersiz olduğu durumlarda, yarı denetimli öğrenme yöntemleri kullanılarak model başarısı artırılmaya çalışılır. Bu çalışmada, bir yarı denetimli öğrenme yöntemi olan kendi kendine öğrenmenin katkısı değerlendirilmiştir. GTZAN veri kümesi ile yapılan deneysel çalışmada, sekiz ayrı sınıflandırıcıda kendi kendine öğrenme yönteminin model başarısına etkisi ölçümlenmiştir. Yapılan deneysel çalışmalar sonucunda, veri kümesi ve kullanılan sınıflandırıcı gibi belirli kriterlerle bağlı olarak kendi kendine öğrenme yönteminin model performansı üzerinde olumlu etkisi olabileceği görülmüştür.