Analyzing time-varying spectral characteristics of speech with function-on-scalar regression

Datos Bibliográficos

ID4579684
AutoresRasmus Puggaard-Rode (0000-0003-4522-9987, Netherlands Graduate School of Linguistics, autor de correspondencia)
Año2022
Volumen95
Páginas101191
Fecha de publicación2022-11-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaJournal of Phonetics (JOURNAL)
Identificadores de la revistaISSN: 0095-4470 • E-ISSN: 1095-8576
EditorialElsevier BV (PUBLISHER)
DOI10.1016/j.wocn.2022.101191
OpenAlexW4303684423
IdiomaEN
Citas recibidas4
Referencias citadas96

The acoustic characteristics of noise from fricatives and stop releases are difficult to analyze. The spectral characteristics of such noise are multi-dimensional, and popular methods for analyzing them typically rely on reducing this complex information to one or a few discrete numbers, such as spectral moments or coefficients of discrete cosine transformations. In this paper, I propose using function-on-scalar regression models as a method for analyzing and mass-comparing spectra with minimal reduction of the complexity in the signal. The method is further useful for analyzing how spectra change as a function of time. The usefulness of this method is demonstrated with a corpus analysis of Danish aspirated stop releases, using the DanPASS corpus. The analysis finds that /t/ releases are invariably affricated; /k/ releases are highly affected by coarticulatory context; and /p/ releases are almost always dominated by aspiration in the latter half of the release, but are affricated in the first half in certain contexts

Algorithm · Context (archaeology) · Function (biology) · Noise (video) · Noise reduction · Physics · Regression analysis · Scalar (mathematics) · Spectral analysis · Speech recognition · Statistics · Trigonometric functions · Artificial Intelligence · Computer Science · Mathematics · Phonetics and Phonology Research · Speech and Audio Processing · Speech Recognition and Synthesis