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.

本研究结合之前未标注的语音数据通过采用 CTC 预训练的两阶段方法生成训练数据，使用 CTC 和 attention Transformer 模型对德语语音识别进行训练，实现了 12.8％的识别错误率，超过了传统混合 DNN / HMM ASR 的 14.4％的基础水平。