Mispronunciation detection and diagnosis (MDD) is designed to identify pronunciation errors and provide instructive feedback to guide non-native language learners, which is a core component in computer-assisted pronunciation training (CAPT) systems. However, MDD often suffers from the data-sparsity problem due to that collecting non-native data and the associated annotations is time-consuming and labor-intensive. To address this issue, we explore a fully end-to-end (E2E) neural model for MDD, which processes learners' speech directly based on raw waveforms. Compared to conventional hand-crafted acoustic features, raw waveforms retain more acoustic phenomena and potentially can help neural networks discover better and more customized representations. To this end, our MDD model adopts a co-called SincNet module to take input a raw waveform and covert it to a suitable vector representation sequence. SincNet employs the cardinal sine (sinc) function to implement learnable bandpass filters, drawing inspiration from the convolutional neural network (CNN). By comparison to CNN, SincNet has fewer parameters and is more amenable to human interpretation. Extensive experiments are conducted on the L2-ARCTIC dataset, which is a publicly-available non-native English speech corpus compiled for research on CAPT. We find that the sinc filters of SincNet can be adapted quickly for non-native language learners of different nationalities. Furthermore, our model can achieve comparable mispronunciation detection performance in relation to state-of-the-art E2E MDD models that take input the standard handcrafted acoustic features. Besides that, our model also provides considerable improvements on phone error rate (PER) and diagnosis accuracy.

中文翻译：

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原始波形的端到端错误诊断和诊断

错误发音检测和诊断（MDD）旨在识别发音错误并提供指导性反馈，以指导非母语学习者，这是计算机辅助发音训练（CAPT）系统的核心组成部分。但是，由于收集非本机数据以及相关的注释既费时又费力，因此MDD经常会遇到数据稀疏的问题。为了解决这个问题，我们探索了MDD的完全端到端（E2E）神经模型，该模型直接基于原始波形来处理学习者的语音。与传统的手工声学特征相比，原始波形保留了更多的声学现象，并有可能帮助神经网络发现更好，更个性化的表示形式。为此，我们的MDD模型采用了一个称为SincNet的模块来输入原始波形并将其转换为合适的矢量表示序列。SincNet利用基本正弦（sinc）函数来实现可学习的带通滤波器，并从卷积神经网络（CNN）中获得启发。与CNN相比，SincNet的参数更少，更易于人工解释。在L2-ARCTIC数据集上进行了广泛的实验，该数据集是为CAPT研究而编写的可公开获得的非母语英语语音语料库。我们发现，SincNet的Sinc过滤器可以快速适应不同国籍的非母语学习者。此外，与采用标准手工声学特征输入的最新E2E MDD模型相比，我们的模型可以实现可比的错误发音检测性能。除此之外，我们的模型还极大地改善了电话错误率（PER）和诊断准确性。