{"ID":22952662,"CreatedAt":"2026-09-17T02:12:05.498442134Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.18686","arxiv_id":"2609.18686","title":"Multi-Teacher Distillation for Cross-Domain Streaming Electrolaryngeal Speech Encoding","abstract":"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.","short_abstract":"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 framew...","url_abs":"https://arxiv.org/abs/2609.18686","url_pdf":"https://arxiv.org/pdf/2609.18686v1","authors":"[\"Benedikt Mayrhofer\",\"Enrique Orozco Olivares\",\"Franz Pernkopf\",\"Philipp Aichinger\",\"Martin Hagmüller\"]","published":"2026-09-16T14:00:38Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[\"Transformer\"]","has_code":false}