@article{MunnichDederichsKleinetal.2023, author = {Munnich, Patrick and Dederichs, Stefanie and Klein, Paula and Bruno Figura, Carl}, title = {Speech Intelligibility: Lateral Lisp Detection for Real-Time Monitoring}, series = {Current Directions in Biomedical Engineering}, volume = {9}, journal = {Current Directions in Biomedical Engineering}, number = {1}, publisher = {De Gruyter}, issn = {2364-5504}, doi = {10.1515/cdbme-2023-1071}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:due62-opus-49407}, pages = {283 -- 286}, year = {2023}, abstract = {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 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.}, subject = {Lispeln}, language = {en} }