The automated classification of stuttered speech has significant implications
for timely assessments providing assistance to speech language pathologists.
Despite notable advancements in the field, the cases in which multiple
disfluencies occur in speech require attention. We have taken a progressive
approach to fill this gap by classifying multi-stuttered speech more
efficiently. The problem has been addressed by firstly curating a dataset of
multi-stuttered disfluencies from SEP-28k audio clips. Secondly, employing
Whisper, a state-of-the-art speech recognition model has been leveraged by
using its encoder and taking the problem as multi-label classification.
Thirdly, using a 6 encoder layer Whisper and experimenting with various layer
freezing strategies, a computationally efficient configuration of the model was
identified. The proposed configuration achieved micro, macro, and weighted F1-
scores of 0.88, 0.85, and 0.87, correspondingly on an external test dataset
i.e. Fluency-Bank. In addition, through layer freezing strategies, we were able
to achieve the aforementioned results by fine-tuning a single encoder layer,
consequently, reducing the model's trainable parameters from 20.27 million to
3.29 million. This research study unveils the contribution of the last encoder
layer in the identification of disfluencies in stuttered speech. Consequently,
it has led to a computationally efficient approach which makes the model more
adaptable for various dialects and languages.

通过使用先进的语音识别模型 Whisper 以及 6 层编码器的不同层冻结策略，本研究提出了一种计算高效的模型配置来更有效地对多发性口吃语音进行自动分类，取得了显著的性能，同时发现最后一个编码器层在辨别口吃性不流畅语音方面的贡献，使模型适用于多种方言和语言。