3 research outputs found
You Do Not Need More Data: Improving End-To-End Speech Recognition by Text-To-Speech Data Augmentation
Data augmentation is one of the most effective ways to make end-to-end
automatic speech recognition (ASR) perform close to the conventional hybrid
approach, especially when dealing with low-resource tasks. Using recent
advances in speech synthesis (text-to-speech, or TTS), we build our TTS system
on an ASR training database and then extend the data with synthesized speech to
train a recognition model. We argue that, when the training data amount is
relatively low, this approach can allow an end-to-end model to reach hybrid
systems' quality. For an artificial low-to-medium-resource setup, we compare
the proposed augmentation with the semi-supervised learning technique. We also
investigate the influence of vocoder usage on final ASR performance by
comparing Griffin-Lim algorithm with our modified LPCNet. When applied with an
external language model, our approach outperforms a semi-supervised setup for
LibriSpeech test-clean and only 33% worse than a comparable supervised setup.
Our system establishes a competitive result for end-to-end ASR trained on
LibriSpeech train-clean-100 set with WER 4.3% for test-clean and 13.5% for
test-other
調波音打楽器音分離による歌声のスペクトルゆらぎに基づく音楽信号処理の研究
学位の種別:課程博士University of Tokyo(東京大学