Automatic detection and severity level classification of dysarthria directly
from acoustic speech signals can be used as a tool in medical diagnosis. In
this work, the pre-trained wav2vec 2.0 model is studied as a feature extractor
to build detection and severity level classification systems for dysarthric
speech. The experiments were carried out with the popularly used UA-speech
database. In the detection experiments, the results revealed that the best
performance was obtained using the embeddings from the first layer of the
wav2vec model that yielded an absolute improvement of 1.23% in accuracy
compared to the best performing baseline feature (spectrogram). In the studied
severity level classification task, the results revealed that the embeddings
from the final layer gave an absolute improvement of 10.62% in accuracy
compared to the best baseline features (mel-frequency cepstral coefficients).

通过使用预训练的 wav2vec 2.0 模型作为特征提取器，本研究对发音困难症的声学语音信号进行了自动检测和严重程度分类，结果表明使用 wav2vec 模型的第一层嵌入特征在准确度上相较于基线特征（声谱图）提升了 1.23%，在严重程度分类任务中最终层的嵌入特征相较于基线特征（梅尔频率倒谱系数）提升了 10.62%。

基于 Wav2vec 的言语智能识别与严重程度分类 —— 以口吃为例

Wav2vec-based Detection and Severity Level Classification of Dysarthria  from Speech

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。