Automatic speech recognition (ASR) systems typically use handcrafted feature
extraction pipelines. To avoid their inherent information loss and to achieve
more consistent modeling from speech to transcribed text, neural raw waveform
feature extractors (FEs) are an appealing approach. Also the wav2vec 2.0 model,
which has recently gained large popularity, uses a convolutional FE which
operates directly on the speech waveform. However, it is not yet studied
extensively in the literature. In this work, we study its capability to replace
the standard feature extraction methods in a connectionist temporal
classification (CTC) ASR model and compare it to an alternative neural FE. We
show that both are competitive with traditional FEs on the LibriSpeech
benchmark and analyze the effect of the individual components. Furthermore, we
analyze the learned filters and show that the most important information for
the ASR system is obtained by a set of bandpass filters.

该研究论文探讨了利用神经原始波形特征提取器替代传统特征提取方法以及其对自动语音识别系统的影响，并与另一种神经特征提取器进行对比分析，结果表明两者在 LibriSpeech 基准测试上与传统特征提取器具有竞争力，并且分析了所学习的滤波器对 ASR 系统的重要信息的作用。