  This paper presents a class of new fast non-trainable entropy-based
confidence estimation methods for automatic speech recognition. We show how
per-frame entropy values can be normalized and aggregated to obtain a
confidence measure per unit and per word for Connectionist Temporal
Classification (CTC) and Recurrent Neural Network Transducer (RNN-T) models.
Proposed methods have similar computational complexity to the traditional
method based on the maximum per-frame probability, but they are more
adjustable, have a wider effective threshold range, and better push apart the
confidence distributions of correct and incorrect words. We evaluate the
proposed confidence measures on LibriSpeech test sets, and show that they are
up to 2 and 4 times better than confidence estimation based on the maximum
per-frame probability at detecting incorrect words for Conformer-CTC and
Conformer-RNN-T models, respectively.
