Sep 16, 2026, cs.SDJ/K move · Enter open · S save
Benedikt Mayrhofer, Enrique Orozco Olivares, Franz Pernkopf, Philipp Aichinger+1
Signal Processing and Speech Communication Laboratory, Graz University of Technology · Comprehensive Center for AI in Medicine, Medical University of Vienna · Dept. of Otolaryngology, Head & Neck Surgery, Div. of Phoniatrics-Logopedics, Speech & Hearing Sci. Lab, Med. Univ. of Vienna
Self-supervised learning (SSL) has improved speech representations, yet performance degrades in pathological domains such as electrolaryngeal (EL) speech, and the computational footprint of SSL models limits their applicability in real-time, on-device deployment. We propose a multi-teacher knowledge distillation framework to train a lightweight, streaming content encoder that generalizes across healthy (HE) and EL speech. Two teachers are distilled progressively: a frozen SSL model providing discrete phonetic cluster targets from HE speech, and an EL-fine-tuned speech recognition model supplying continuous bottleneck feature targets. Evaluated via downstream speech recognition, our approach reduces the EL word error rate to 21.2%, compared to 39.3% for the strongest zero-shot SSL baseline. Among causal convolutional, Transformer, Conformer, and Mamba-based student architectures, a Mel-Conformer achieves the best combination of EL accuracy and computational efficiency. The final encoder contains 21.9,M parameters and runs at a real-time factor of 0.30 under ONNX Runtime on a single CPU core.