Modeling Overlapped Speech with Shuffles
We propose to model parallel streams of data, such as overlapped speech, using shuffles. Specifically, this paper shows how the shuffle product and partial order finite-state automata (FSAs) can be used for alignment and speaker-attributed transcription of overlapped speech. We train using the total score on these FSAs as a loss function, marginalizing over all possible serializations of overlapping sequences at subword, word, and phrase levels. To reduce graph size, we impose temporal constraints by constructing partial order FSAs. We address speaker attribution by modeling (token, speaker) tuples directly. Viterbi alignment through the shuffle product FSA directly enables one-pass align...
| Authors | Matthew Wiesner, Samuele Cornell, Alexander Polok et al. |
|---|---|
| Primary category | cs.SD |
| Published | 18 March 2026 |
| Last updated at source | 18 March 2026 |
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