End-to-End Mispronunciation Detection and Diagnosis From Raw Waveforms

by   Bi-Cheng Yan, et al.

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.



There are no comments yet.


page 1


Speech and Speaker Recognition from Raw Waveform with SincNet

Deep neural networks can learn complex and abstract representations, tha...

Towards Robust Mispronunciation Detection and Diagnosis for L2 English Learners with Accent-Modulating Methods

With the acceleration of globalization, more and more people are willing...

Multi-Span Acoustic Modelling using Raw Waveform Signals

Traditional automatic speech recognition (ASR) systems often use an acou...

Improving End-To-End Modeling for Mispronunciation Detection with Effective Augmentation Mechanisms

Recently, end-to-end (E2E) models, which allow to take spectral vector s...

Exploring Non-Autoregressive End-To-End Neural Modeling For English Mispronunciation Detection And Diagnosis

End-to-end (E2E) neural modeling has emerged as one predominant school o...

Corrective feedback, emphatic speech synthesis, visual-speech exaggeration, pronunciation learning

To provide more discriminative feedback for the second language (L2) lea...

Replay attack detection with complementary high-resolution information using end-to-end DNN for the ASVspoof 2019 Challenge

In this study, we concentrate on replacing the process of extracting han...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.