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Emitting Word Timings with HMM-Free End-to-End System in Automatic Speech Recognition

Word timings, which mark the start and end times of each word in ASR results, play an important part in many ap-plications, such as computer assisted language learning. To date, end-to-end (E2E) systems outperform conventional DNN-HMM hybrid systems in ASR accuracy but have challenges to obtain accurate word timings. In this paper, we propose a two-pass method to estimate word timings under an E2E-based LAS modeling framework, which is completely free of using the DNN-HMM ASR system. Speciﬁcally, we ﬁrst employ the LAS system to obtain word-piece transcripts of the input audio, we then compute forced-alignments with a frame-level-based word-piece classiﬁer. In order to make the classiﬁer yield accurate word-piece timing results, we propose a novel objective function to learn the classiﬁer, utilizing the spike timings of the connectionist temporal classiﬁcation (CTC) model. On Librispeech data, our E2E-based LAS system achieves 2.8%/7.0% WERs, while its word timing (start/end) accuracy are 99.0%/95.3% and 98.6%/93.7% on test-clean and test-other two test sets respectively. Compared with a DNN-HMM hybrid ASR system (here, TDNN), the LAS system is better in ASR performance, and the generated word timings are close to what the TDNN ASR system presents.