Year: 2021
Type: conference-paper
Abstract: With the success of neural network based modeling in auto-matic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined stan-dard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained su-pervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an ex-isting acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance.
Authors: Peter Vieting, Christoph Luscher, Wilfried Michel, Ralf Schluter, Hermann Ney
Institution: RWTH Aachen University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: H2020 European Research Council
OpenAlex ID: W3204844985