Speech event detector: Timestamped detection of non-verbal vocalizations and background sounds for rich speech transcription
Automatic speech recognition produces words but drops the non-verbal content of a recording: the breaths, coughs, sighs and laughter of the speaker, and the horns, music or barking around them. We describe Speech Event Detector, a 5.8-million-parameter convolutional-Transformer model that outputs probabilities every 40 ms for 54 event classes (20 speaker vocalizations and 34 background sounds), together with post-processing that converts these probabilities into timestamped events and a procedure for merging the events into a Whisper transcript. The model is trained on material drawn from seven public datasets whose labels come in incompatible forms: single detected regions, free-text tags w
| Authors | Ayush Kumar Bar, Promila Ghosh |
|---|---|
| Primary category | |
| Published | 9 October 2026 |
| Last updated at source | — |
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