In recent years, advancements in the field of speech processing have led to
cutting-edge deep learning algorithms with immense potential for real-world
applications. The automated identification of stuttered speech is one of such
applications that the researchers are addressing by employing deep learning
techniques. Recently, researchers have utilized Wav2vec2.0, a speech
recognition model to classify disfluency types in stuttered speech. Although
Wav2vec2.0 has shown commendable results, its ability to generalize across all
disfluency types is limited. In addition, since its base model uses 12 encoder
layers, it is considered a resource-intensive model. Our study unravels the
capabilities of Whisper for the classification of disfluency types in stuttered
speech. We have made notable contributions in three pivotal areas: enhancing
the quality of SEP28-k benchmark dataset, exploration of Whisper for
classification, and introducing an efficient encoder layer freezing strategy.
The optimized Whisper model has achieved the average F1-score of 0.81, which
proffers its abilities. This study also unwinds the significance of deeper
encoder layers in the identification of disfluency types, as the results
demonstrate their greater contribution compared to initial layers. This
research represents substantial contributions, shifting the emphasis towards an
efficient solution, thereby thriving towards prospective innovation.

近年来，在语音处理领域的进展已经引发出具有巨大潜力的尖端深度学习算法，而自动识别口吃症言语是这些研究者采用深度学习技术所致力解决的应用之一。本研究通过旨在聚焦于高效解决方案的重要贡献，解开了 Whisper 在口吃症言语类型分类上的能力，并对较深层编码器的重要性进行了探究。