Literature DB >> 15764212

Effect on LTAS of vocal loudness variation.

Maria Nordenberg1, Johan Sundberg.   

Abstract

Long-term-average spectrum (LTAS) is an efficient method for voice analysis, revealing both voice source and formant characteristics. However, the LTAS contour is non-uniformly affected by vocal loudness. This variation was analyzed in 15 male and 16 female untrained voices reading a text 7 times at different degrees of vocal loudness, mean change in overall equivalent sound level (Leq) amounting to 27.9 dB and 28.4 dB for the female and male subjects. For all frequency values up to 4 kHz, spectrum level was strongly and linearly correlated with Leq for each subject. The gain factor, that is to say, the rate of level increase, varied with frequency, from about 0.5 at low frequencies to about 1.5 in the frequency range 1.5-3 kHz. Using the gain factors for a subject, LTAS contours could be predicted at any Leq within the measured range, with an average accuracy of 2-3 dB below 4 kHz. Mean LTAS calculated for an Leq of 70 dB for each subject showed considerable individual variation for both males and females, SD of the level varying between 7 dB and 4 dB depending on frequency. On the other hand, the results also suggest that meaningful comparisons of LTAS, recorded for example before and after voice therapy, can be made, provided that the documentation includes a set of recordings at different loudness levels from one recording session.

Mesh:

Year:  2004        PMID: 15764212     DOI: 10.1080/14015430410004689

Source DB:  PubMed          Journal:  Logoped Phoniatr Vocol        ISSN: 1401-5439            Impact factor:   1.487


  3 in total

1.  Long-Term Average Spectral (LTAS) Measures of Dysarthria and Their Relationship to Perceived Severity.

Authors:  Kris Tjaden; Joan E Sussman; Grace Liu; Greg Wilding
Journal:  J Med Speech Lang Pathol       Date:  2010-12

2.  Lower Vocal Tract Morphologic Adjustments Are Relevant for Voice Timbre in Singing.

Authors:  Alexander Mainka; Anton Poznyakovskiy; Ivan Platzek; Mario Fleischer; Johan Sundberg; Dirk Mürbe
Journal:  PLoS One       Date:  2015-07-17       Impact factor: 3.240

3.  Vocal features obtained through automated methods in verbal fluency tasks can aid the identification of mixed episodes in bipolar disorder.

Authors:  Luisa Weiner; Andrea Guidi; Nadège Doignon-Camus; Anne Giersch; Gilles Bertschy; Nicola Vanello
Journal:  Transl Psychiatry       Date:  2021-08-02       Impact factor: 6.222

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.