  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.
