In this paper, we present a novel approach for text independent
phone-to-audio alignment based on phoneme recognition, representation learning
and knowledge transfer. Our method leverages a self-supervised model (wav2vec2)
fine-tuned for phoneme recognition using a Connectionist Temporal
Classification (CTC) loss, a dimension reduction model and a frame-level
phoneme classifier trained thanks to forced-alignment labels (using Montreal
Forced Aligner) to produce multi-lingual phonetic representations, thus
requiring minimal additional training. We evaluate our model using synthetic
native data from the TIMIT dataset and the SCRIBE dataset for American and
British English, respectively. Our proposed model outperforms the
state-of-the-art (charsiu) in statistical metrics and has applications in
language learning and speech processing systems. We leave experiments on other
languages for future work but the design of the system makes it easily
adaptable to other languages.

基于音素识别、表示学习和知识迁移，本文提出了一种新颖的文本无关音频对齐方法，利用自监督模型、维度降低模型和基于帧级音素分类器的强制对齐标签来产生多语言语音学表示，取得了优于传统方法的统计指标表现，在语言学习和语音处理系统中有应用意义。