One of the biggest challenges in designing mispronunciation detection models
is the unavailability of labeled L2 speech data. To overcome such data
scarcity, we introduce SpeechBlender -- a fine-grained data augmentation
pipeline for generating mispronunciation errors. The SpeechBlender utilizes
varieties of masks to target different regions of a phonetic unit, and use the
mixing factors to linearly interpolate raw speech signals while generating
erroneous pronunciation instances. The masks facilitate smooth blending of the
signals, thus generating more effective samples than the `Cut/Paste' method. We
show the effectiveness of our augmentation technique in a phoneme-level
pronunciation quality assessment task, leveraging only a good pronunciation
dataset. With SpeechBlender augmentation, we observed a 3% and 2% increase in
Pearson correlation coefficient (PCC) compared to no-augmentation and goodness
of pronunciation augmentation scenarios respectively for Speechocean762
testset. Moreover, a 2% rise in PCC is observed when comparing our single-task
phoneme-level mispronunciation detection model with a multi-task learning model
using multiple-granularity information.

本文介绍了 SpeechBlender 数据扩充方法生成发音错误样本的 Fine-grained 数据增强流水线，在发音质量评估任务中展示了其有效性，表现优于其他数据增强方法。