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。

wav2vec 2.0 特征提取器的比较分析

Comparative Analysis of the wav2vec 2.0 Feature Extractor

Convolutional neural networks (CNN) have shown promising results for
end-to-end speech recognition, albeit still behind other state-of-the-art
methods in performance. In this paper, we study how to bridge this gap and go
beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet.
ContextNet features a fully convolutional encoder that incorporates global
context information into convolution layers by adding squeeze-and-excitation
modules. In addition, we propose a simple scaling method that scales the widths
of ContextNet that achieves good trade-off between computation and accuracy. We
demonstrate that on the widely used LibriSpeech benchmark, ContextNet achieves
a word error rate (WER) of 2.1%/4.6% without external language model (LM),
1.9%/4.1% with LM and 2.9%/7.0% with only 10M parameters on the clean/noisy
LibriSpeech test sets. This compares to the previous best published system of
2.0%/4.6% with LM and 3.9%/11.3% with 20M parameters. The superiority of the
proposed ContextNet model is also verified on a much larger internal dataset.

本文报道了一种新的 CNN-RNN-transducer 结构 ContextNet，它包括一个全卷积编码器，通过添加 squeeze-and-excitation 模块将全局上下文信息嵌入到卷积层中，以及一个简单的缩放方法，实现了计算和准确度之间的良好折衷，这种模型在多个基准测试集上显著优于其他模型。

ContextNet：利用全局上下文提升卷积神经网络在自动语音识别中的表现

ContextNet: Improving Convolutional Neural Networks for Automatic Speech  Recognition with Global Context

We propose and evaluate transformer-based acoustic models (AMs) for hybrid
speech recognition. Several modeling choices are discussed in this work,
including various positional embedding methods and an iterated loss to enable
training deep transformers. We also present a preliminary study of using
limited right context in transformer models, which makes it possible for
streaming applications. We demonstrate that on the widely used Librispeech
benchmark, our transformer-based AM outperforms the best published hybrid
result by 19% to 26% relative when the standard n-gram language model (LM) is
used. Combined with neural network LM for rescoring, our proposed approach
achieves state-of-the-art results on Librispeech. Our findings are also
confirmed on a much larger internal dataset.

本文提出并评估了基于 transformer 的声学模型在混合语音识别中的应用。通过讨论不同的建模选择，包括各种位置嵌入方法及迭代损失等，我们证明了在 Librispeech 基准测试中使用 transformer 模型相比其他模型性能更优越，且结合神经网络进行二次评分，我们的方法可以达到最先进的结果。