title: On Architectures and Training for Raw Waveform Feature Extraction in ASR
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
journal: 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)