  The adoption of advanced deep learning (DL) architecture in stuttering
detection (SD) tasks is challenging due to the limited size of the available
datasets. To this end, this work introduces the application of speech
embeddings extracted with pre-trained deep models trained on massive audio
datasets for different tasks. In particular, we explore audio representations
obtained using emphasized channel attention, propagation, and
aggregation-time-delay neural network (ECAPA-TDNN) and Wav2Vec2.0 model trained
on VoxCeleb and LibriSpeech datasets respectively. After extracting the
embeddings, we benchmark with several traditional classifiers, such as a
k-nearest neighbor, Gaussian naive Bayes, and neural network, for the
stuttering detection tasks. In comparison to the standard SD system trained
only on the limited SEP-28k dataset, we obtain a relative improvement of 16.74%
in terms of overall accuracy over baseline. Finally, we have shown that
combining two embeddings and concatenating multiple layers of Wav2Vec2.0 can
further improve SD performance up to 1% and 2.64% respectively.
