Accurate and Reliable Confidence Estimation Based on Non-Autoregressive End-to-End Speech Recognition System
This work addresses a specific bottleneck in ASR confidence estimation for downstream tasks, representing an incremental improvement over previous methods.
The paper tackled the problem of unreliable confidence estimation in end-to-end speech recognition when deletion and insertion errors occur, by proposing a CIF-Aligned confidence estimation model that achieved 24% and 19% relative reductions in error metrics on test sets.
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.