CTC-Segmentation of Large Corpora for German End-to-End Speech Recognition

• Published in: Speech and Computer Vol. 12335; pp. 267 - 278
• Main Authors: Kürzinger, Ludwig, Winkelbauer, Dominik, Li, Lujun, Watzel, Tobias, Rigoll, Gerhard
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2020; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: CTC-segmentation; End-to-end automatic speech recognition; German speech dataset; Hybrid CTC/Attention
• ISBN: 3030602753; 9783030602758
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-030-60276-5_27

Recent end-to-end Automatic Speech Recognition (ASR) systems demonstrated the ability to outperform conventional hybrid DNN/HMM ASR. Aside from architectural improvements in those systems, those models grew in terms of depth, parameters and model capacity. However, these models also require more training data to achieve comparable performance. In this work, we combine freely available corpora for German speech recognition, including yet unlabeled speech data, to a big dataset of over 1700 h of speech data. For data preparation, we propose a two-stage approach that uses an ASR model pre-trained with Connectionist Temporal Classification (CTC) to boot-strap more training data from unsegmented or unlabeled training data. Utterances are then extracted from label probabilities obtained from the network trained with CTC to determine segment alignments. With this training data, we trained a hybrid CTC/attention Transformer model that achieves 12.8% WER on the Tuda-DE test set, surpassing the previous baseline of 14.4% of conventional hybrid DNN/HMM ASR.