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This paper proposes an adaptation method for end-to-end speech recognition.In this method, multiple automatic speech recognition (ASR) 1-best hypothesesare integrated in the computation of the connectionist temporal classification(CTC) loss function. The integration of multiple ASR hypotheses helpsalleviating the impact of errors in the ASR hypotheses to the computation ofthe CTC loss when ASR hypotheses are used. When being applied insemi-supervised adaptation scenarios where part of the adaptation data do nothave labels, the CTC loss of the proposed method is computed from different ASR1-best hypotheses obtained by decoding the unlabeled adaptation data.Experiments are performed in clean and multi-condition training scenarios wherethe CTC-based end-to-end ASR systems are trained on Wall Street Journal (WSJ)clean training data and CHiME-4 multi-condition training data, respectively,and tested on Aurora-4 test data. The proposed adaptation method yields 6.6%and 5.8% relative word error rate (WER) reductions in clean and multi-conditiontraining scenarios, respectively, compared to a baseline system which isadapted with part of the adaptation data having manual transcriptions usingback-propagation fine-tuning.

Multiple-hypothesis CTC-based semi-supervised adaptation of end-to-end speech recognition