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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

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Cross-Correlation Based Automated Segmentation of Audio Samples.

Emilian-Erman Mahmut¹, Stelian Nicola¹, Vasile Stoicu-Tivadar¹

¹Department of Automation and Applied Informatics Politehnica University Timisoara, Romania.

Studies in Health Technology and Informatics

|July 2, 2020

Summary

This study introduces an audio segmentation method to standardize speech samples for screening systems. It ensures consistent pronunciation samples, improving automated analysis for speech disorders.

Keywords:

Phonological assimilation audio segmentation cross-correlation

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Area of Science:

Speech processing
Computational linguistics
Biomedical engineering

Background:

Variability in utterance duration complicates automated speech analysis.
Accurate segmentation is crucial for reliable speech disorder screening.

Purpose of the Study:

To develop an automated audio segmentation method for standardizing speech samples.
To address challenges in analyzing speech from individuals with varying durations, such as Speech-Language Pathologists (SLP) and dyslalic subjects.
To provide homogeneously-trimmed audio samples for computerized Speech Sound Disorder (SSD) screening systems.

Main Methods:

Determining the maximum cross-correlation value between two audio files.
Automated segmentation based on cross-correlation to extract target consonant pronunciation samples.
Pre-processing audio files to create uniform sample lengths.

Main Results:

The method successfully segmented 30 pronunciations.
The automated segmentation provided consistent audio samples for screening.

Conclusions:

The proposed audio segmentation method effectively standardizes speech samples.
This technique can enhance the accuracy and reliability of computerized SSD screening systems.