Cross-Correlation Based Automated Segmentation of Audio Samples
Emilian-Erman Mahmut, Stelian Nicola, Vasile Stoicu-Tivadar · Studies in health technology and informatics · 2020
This paper presents an audio file segmentation method in an attempt to mitigate the issue of variable durations of the same utterance by different individuals, e.g.: Speech-Language Pathologist (SLP) and dyslalic subjects. The Method section describes the manner of determination of the maximum cross-correlation value between the 2 audio files and the subsequent automated segmentation thereof in order to extract 2 valid pronunciation samples of the target consonant. The method is aimed at pre-processing audio files and supplying homogeneously-trimmed audio samples to a computerized SSD Screening system. The results obtained on a batch of 30 pronunciations are presented and briefly discussed in the third section while the last section is reserved for conclusions and perspectives.