End-to-End Voice Intent Recognition for Spontaneous Human-Drone Interaction with Naive Users
Voice control offers an intuitive alternative to manual drone piloting, yet most existing systems rely on rigid command vocabularies that fail to handle the spontaneous, disfluent speech of naive users. This paper addresses this gap by proposing an End-to-End Spoken Language Understanding architecture for real-time human-drone interaction in French. Our model combines a frozen Self-Supervised Learning acoustic encoder with a lightweight LSTM-based classification head, augmented by a cross-modal knowledge distillation objective that aligns acoustic representations with semantic embeddings from a text teacher, without requiring transcription at inference time. We evaluate our approach on Vo...
| Authors | Allan Henry, Solange Rossato, Christian Graff et al. |
|---|---|
| Primary category | eess.AS |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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