  Estimating confidence scores for recognition results is a classic task in ASR
field and of vital importance for kinds of downstream tasks and training
strategies. Previous end-to-end~(E2E) based confidence estimation models (CEM)
predict score sequences of equal length with input transcriptions, leading to
unreliable estimation when deletion and insertion errors occur. In this paper
we proposed CIF-Aligned confidence estimation model (CA-CEM) to achieve
accurate and reliable confidence estimation based on novel non-autoregressive
E2E ASR model - Paraformer. CA-CEM utilizes the modeling character of
continuous integrate-and-fire (CIF) mechanism to generate token-synchronous
acoustic embedding, which solves the estimation failure issue above. We measure
the quality of estimation with AUC and RMSE in token level and ECE-U - a
proposed metrics in utterance level. CA-CEM gains 24% and 19% relative
reduction on ECE-U and also better AUC and RMSE on two test sets. Furthermore,
we conduct analysis to explore the potential of CEM for different ASR related
usage.
