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Phonetic analysis of speech, in general, requires the alignment of audio samples to its phonetic transcription. This could be done manually for a couple of files, but as the corpus grows large, it becomes infeasibly time-consuming. This paper describes the evolution process toward creating free resources for phonetic alignment in Brazilian Portuguese (BP) using Kaldi, a toolkit that achieves state of the art for open-source speech recognition, within a toolkit we call UFPAlign. The contributions of this work are then twofold: developing resources to perform forced alignment in BP, including the release of scripts to train acoustic models via Kaldi, as well as the resources themselves under open licenses; and bringing forth a comparison to other two phonetic aligners that provide resources for BP, namely EasyAlign and Montreal Forced Aligner (MFA), the latter being also Kaldi-based. Evaluation took place in terms of phone boundary and intersection over union metrics over a dataset of 385 hand-aligned utterances, and results show that Kaldi-based aligners perform better overall, and that UFPAlign models are more accurate than MFA’s. Furthermore, complex deep-learning-based approaches still do not improve performance compared to simpler models.

Free resources for forced phonetic alignment in Brazilian Portuguese based on Kaldi toolkit