Active learning for automatic speech recognition

Abstract

State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor intensive and time-consuming. In this paper, we describe a new method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled by automatically processing the unlabeled examples, and then selecting the most informative ones with respect to a given cost function for a human to label. We automatically estimate a confidence score for each word of the utterance, exploiting the lattice output of a speech recognizer, which was trained on a small set of transcribed data. We compute utterance confidence scores based on these word confidence scores, then selectively sample the utterances to be transcribed using the utterance confidence scores. In our experiments, we show that we reduce the amount of labeled data needed for a given word accuracy by 27%.