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Conventional ASR systems use frame-level phoneme posterior to conductforce-alignment~(FA) and provide timestamps, while end-to-end ASR systemsespecially AED based ones are short of such ability. This paper proposes toperform timestamp prediction~(TP) while recognizing by utilizing continuousintegrate-and-fire~(CIF) mechanism in non-autoregressive ASR model -Paraformer. Foucing on the fire place bias issue of CIF, we conductpost-processing strategies including fire-delay and silence insertion. Besides,we propose to use scaled-CIF to smooth the weights of CIF output, which isproved beneficial for both ASR and TP task. Accumulated averaging shift~(AAS)and diarization error rate~(DER) are adopted to measure the quality oftimestamps and we compare these metrics of proposed system and conventionalhybrid force-alignment system. The experiment results over manually-markedtimestamps testset show that the proposed optimization methods significantlyimprove the accuracy of CIF timestamps, reducing 66.7\% and 82.1\% of AAS andDER respectively. Comparing to Kaldi force-alignment trained with the samedata, optimized CIF timestamps achieved 12.3\% relative AAS reduction.

Achieving Timestamp Prediction While Recognizing with Non-Autoregressive End-to-End ASR Model