  Confidence measure is a performance index of particular importance for
automatic speech recognition (ASR) systems deployed in real-world scenarios. In
the present study, utterance-level neural confidence measure (NCM) in
end-to-end automatic speech recognition (E2E ASR) is investigated. The E2E
system adopts the joint CTC-attention Transformer architecture. The prediction
of NCM is formulated as a task of binary classification, i.e., accept/reject
the input utterance, based on a set of predictor features acquired during the
ASR decoding process. The investigation is focused on evaluating and comparing
the efficacies of predictor features that are derived from different internal
and external modules of the E2E system. Experiments are carried out on children
speech, for which state-of-the-art ASR systems show less than satisfactory
performance and robust confidence measure is particularly useful. It is noted
that predictor features related to acoustic information of speech play a more
important role in estimating confidence measure than those related to
linguistic information. N-best score features show significantly better
performance than single-best ones. It has also been shown that the metrics of
EER and AUC are not appropriate to evaluate the NCM of a mismatched ASR with
significant performance gap.
