Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices

The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of voice 2023-09
Hauptverfasser: Llico, Andres F., Shanley, Savannah N., Friedman, Aaron D., Bamford, Leigh M., Roberts, Rachel M., McKenna, Victoria S.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title Journal of voice
container_volume
creator Llico, Andres F.
Shanley, Savannah N.
Friedman, Aaron D.
Bamford, Leigh M.
Roberts, Rachel M.
McKenna, Victoria S.
description The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18–80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68–0.95, with outliers; r2 = 0.80–0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.
doi_str_mv 10.1016/j.jvoice.2023.07.032
format Article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2863764921</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><els_id>S0892199723002412</els_id><sourcerecordid>2863764921</sourcerecordid><originalsourceid>FETCH-LOGICAL-c254t-afaf6917c9da6b589ef6ff35fa52bfeaded05186297c6bb2e59d895255bc17403</originalsourceid><addsrcrecordid>eNp9UMtu3CAURVUqdZL0D7pg2Y1dwMY2m0rTSZqJFKmRknaLMFwyWDZMgUmVvw-TybqrK52X7jkIfaGkpoR236Z6eg5OQ80Ia2rS16RhH9CKDn1TtXwYztCKDIJVVIj-EzpPaSKEsMKukN-EZa-iS8HjH5D_AXi8OaQcFvywqJj3u-ABr3UomNP4PgYNKTn_hNfzU4gu75aElTeFUSpj5_EW1Jx3L2_gVcmNBiIY_Of4YLpEH62aE3x-vxfo98_rx822uvt1c7tZ31Wa8TZXyirbCdprYVQ38kGA7axtuFWcjRaUAUM4HTomet2NIwMuzCA443zUtG9Jc4G-nnL3Mfw9QMpycUnDPCsPpYpkQ9f0XSsYLdL2JNUxpBTByn10pfqLpEQe55WTPM0rj_NK0ssyb7F9P9mg1Hh2EGXSDrwG4yLoLE1w_w94BTphh9Q</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2863764921</pqid></control><display><type>article</type><title>Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices</title><source>Access via ScienceDirect (Elsevier)</source><creator>Llico, Andres F. ; Shanley, Savannah N. ; Friedman, Aaron D. ; Bamford, Leigh M. ; Roberts, Rachel M. ; McKenna, Victoria S.</creator><creatorcontrib>Llico, Andres F. ; Shanley, Savannah N. ; Friedman, Aaron D. ; Bamford, Leigh M. ; Roberts, Rachel M. ; McKenna, Victoria S.</creatorcontrib><description>The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18–80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68–0.95, with outliers; r2 = 0.80–0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.</description><identifier>ISSN: 0892-1997</identifier><identifier>EISSN: 1873-4588</identifier><identifier>DOI: 10.1016/j.jvoice.2023.07.032</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Acoustics ; Smartphone ; Technology ; Voice</subject><ispartof>Journal of voice, 2023-09</ispartof><rights>2023 The Voice Foundation</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c254t-afaf6917c9da6b589ef6ff35fa52bfeaded05186297c6bb2e59d895255bc17403</citedby><cites>FETCH-LOGICAL-c254t-afaf6917c9da6b589ef6ff35fa52bfeaded05186297c6bb2e59d895255bc17403</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jvoice.2023.07.032$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>315,782,786,3552,27931,27932,46002</link.rule.ids></links><search><creatorcontrib>Llico, Andres F.</creatorcontrib><creatorcontrib>Shanley, Savannah N.</creatorcontrib><creatorcontrib>Friedman, Aaron D.</creatorcontrib><creatorcontrib>Bamford, Leigh M.</creatorcontrib><creatorcontrib>Roberts, Rachel M.</creatorcontrib><creatorcontrib>McKenna, Victoria S.</creatorcontrib><title>Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices</title><title>Journal of voice</title><description>The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18–80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68–0.95, with outliers; r2 = 0.80–0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.</description><subject>Acoustics</subject><subject>Smartphone</subject><subject>Technology</subject><subject>Voice</subject><issn>0892-1997</issn><issn>1873-4588</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><recordid>eNp9UMtu3CAURVUqdZL0D7pg2Y1dwMY2m0rTSZqJFKmRknaLMFwyWDZMgUmVvw-TybqrK52X7jkIfaGkpoR236Z6eg5OQ80Ia2rS16RhH9CKDn1TtXwYztCKDIJVVIj-EzpPaSKEsMKukN-EZa-iS8HjH5D_AXi8OaQcFvywqJj3u-ABr3UomNP4PgYNKTn_hNfzU4gu75aElTeFUSpj5_EW1Jx3L2_gVcmNBiIY_Of4YLpEH62aE3x-vxfo98_rx822uvt1c7tZ31Wa8TZXyirbCdprYVQ38kGA7axtuFWcjRaUAUM4HTomet2NIwMuzCA443zUtG9Jc4G-nnL3Mfw9QMpycUnDPCsPpYpkQ9f0XSsYLdL2JNUxpBTByn10pfqLpEQe55WTPM0rj_NK0ssyb7F9P9mg1Hh2EGXSDrwG4yLoLE1w_w94BTphh9Q</recordid><startdate>20230908</startdate><enddate>20230908</enddate><creator>Llico, Andres F.</creator><creator>Shanley, Savannah N.</creator><creator>Friedman, Aaron D.</creator><creator>Bamford, Leigh M.</creator><creator>Roberts, Rachel M.</creator><creator>McKenna, Victoria S.</creator><general>Elsevier Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20230908</creationdate><title>Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices</title><author>Llico, Andres F. ; Shanley, Savannah N. ; Friedman, Aaron D. ; Bamford, Leigh M. ; Roberts, Rachel M. ; McKenna, Victoria S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c254t-afaf6917c9da6b589ef6ff35fa52bfeaded05186297c6bb2e59d895255bc17403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Acoustics</topic><topic>Smartphone</topic><topic>Technology</topic><topic>Voice</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Llico, Andres F.</creatorcontrib><creatorcontrib>Shanley, Savannah N.</creatorcontrib><creatorcontrib>Friedman, Aaron D.</creatorcontrib><creatorcontrib>Bamford, Leigh M.</creatorcontrib><creatorcontrib>Roberts, Rachel M.</creatorcontrib><creatorcontrib>McKenna, Victoria S.</creatorcontrib><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of voice</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Llico, Andres F.</au><au>Shanley, Savannah N.</au><au>Friedman, Aaron D.</au><au>Bamford, Leigh M.</au><au>Roberts, Rachel M.</au><au>McKenna, Victoria S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices</atitle><jtitle>Journal of voice</jtitle><date>2023-09-08</date><risdate>2023</risdate><issn>0892-1997</issn><eissn>1873-4588</eissn><abstract>The aim of this study was to understand the relationship between temporal and spectral-based acoustic measures derived using Praat and custom smartphone algorithms across patients with a wide range of vocal pathologies. Voice samples were collected from 56 adults (11 vocally healthy, 45 dysphonic, aged 18–80 years) performing three speech tasks: (a) sustained vowel, (b) maximum phonation, and (c) the second and third sentences of the Rainbow passage. Data were analyzed to extract mean fundamental frequency (fo), maximum phonation time (MPT), and cepstral peak prominence (CPP) using Praat and our custom smartphone algorithms. Linear regression models were calculated with and without outliers to determine relationships. Statistically significant relationships were found between the smartphone algorithms and Praat for all three measures (r2 = 0.68–0.95, with outliers; r2 = 0.80–0.98, without outliers). An offset between CPP measures was found where Praat values were consistently lower than those computed by the smartphone app. Outlying data were identified and described, and findings indicated that speakers with high levels of clinician-perceived dysphonia resulted in smartphone algorithm errors. These results suggest that the proposed algorithms can provide measurements comparable to clinically derived values. However, clinicians should take caution when analyzing severely dysphonic voices as the current algorithms show reduced accuracy for measures of mean fo and MPT for these voice types.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.jvoice.2023.07.032</doi></addata></record>
fulltext fulltext
identifier ISSN: 0892-1997
ispartof Journal of voice, 2023-09
issn 0892-1997
1873-4588
language eng
recordid cdi_proquest_miscellaneous_2863764921
source Access via ScienceDirect (Elsevier)
subjects Acoustics
Smartphone
Technology
Voice
title Comparison Between Custom Smartphone Acoustic Processing Algorithms and Praat in Healthy and Disordered Voices
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-05T20%3A09%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Comparison%20Between%20Custom%20Smartphone%20Acoustic%20Processing%20Algorithms%20and%20Praat%20in%20Healthy%20and%20Disordered%20Voices&rft.jtitle=Journal%20of%20voice&rft.au=Llico,%20Andres%20F.&rft.date=2023-09-08&rft.issn=0892-1997&rft.eissn=1873-4588&rft_id=info:doi/10.1016/j.jvoice.2023.07.032&rft_dat=%3Cproquest_cross%3E2863764921%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2863764921&rft_id=info:pmid/&rft_els_id=S0892199723002412&rfr_iscdi=true