Speech Intelligibility: Lateral Lisp Detection for Real-Time Monitoring

  • Correcting lisps in speech can prove to be of great difficulty to many, as they may be unaware of whether they are lisping. To help those affected, we have developed a simple algorithm for the real-time identification of the sigmatismus lateralis in ”S” sounds within speech via analysis in the frequency domain. The algorithm identifies peaks within the lisp’s frequency band after calibration. A frequency band of 3000-4000 Hz has been identified to be generally accurate for lisp and 2500-3000 Hz for the correct pronunciation for a single male test subject. The algorithm splits given speech recordings into smaller segments and compares the number of lisps and non-lisps detected in these segments to categorize. From tests, it was concluded that a segment length of 0.5 s produces the best results. The algorithm does not detect every lisp section, however it does not raise false positives. Our implementation in Julia with multi-threaded per-file analysis is able to analyze 20 files ofCorrecting lisps in speech can prove to be of great difficulty to many, as they may be unaware of whether they are lisping. To help those affected, we have developed a simple algorithm for the real-time identification of the sigmatismus lateralis in ”S” sounds within speech via analysis in the frequency domain. The algorithm identifies peaks within the lisp’s frequency band after calibration. A frequency band of 3000-4000 Hz has been identified to be generally accurate for lisp and 2500-3000 Hz for the correct pronunciation for a single male test subject. The algorithm splits given speech recordings into smaller segments and compares the number of lisps and non-lisps detected in these segments to categorize. From tests, it was concluded that a segment length of 0.5 s produces the best results. The algorithm does not detect every lisp section, however it does not raise false positives. Our implementation in Julia with multi-threaded per-file analysis is able to analyze 20 files of lengths between 5 s and 10 s within 0.21 s on a Qualcomm Snapdragon 860 smartphone chipset, meaning analysis is far faster than real-time. The proposed algorithm is a simple prototype algorithm capable of realtime analysis of audio in the frequency domain to identify whether lateral lisps are the dominant sibilant pronunciation in a given window. The method was only tested for a single test subject. However, a calibration algorithm capable of adjusting parameters to new individuals is proposed. The algorithm itself should be easily expandable to identify other speech impediments.show moreshow less

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Author:Patrick MunnichORCiD, Stefanie Dederichs, Paula Klein, Carl Bruno Figura
Qualitätssicherung:peer reviewed
open access:Diamant - ohne Publikationskosten mit Lizenzhinweis
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Medien
Document Type:Article
Year of Completion:2023
Language of Publication:English
Publisher:De Gruyter
Parent Title (English):Current Directions in Biomedical Engineering
Volume:9
Issue:1
Page Number:4
First Page:283
Last Page:286
URN:urn:nbn:de:hbz:due62-opus-49407
DOI:https://doi.org/10.1515/cdbme-2023-1071
ISSN:2364-5504
GND Keyword:Lispeln; Signalverarbeitung
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
Release Date:2025/01/27
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