Time-varying quasi-closed-phase analysis for accurate formant tracking in speech signals
Access rights
openAccess
acceptedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Other link related to publication (opens in new window)
Date
2020-06-04
Major/Subject
Mcode
Degree programme
Language
en
Pages
14
Series
IEEE/ACM Transactions on Audio, Speech, and Language Processing, Volume 28, pp. 1901-1914
Abstract
In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Con-ventional formant tracking methods typically adopt a two-stage estimate-and-track strategy wherein an initial set of formant candidates are estimated using short-time analysis (e.g., 10–50 ms), followed by a tracking stage based on dynamic programming or a linear state-space model. One of the main disadvantages of these approaches is that the tracking stage, however good it may be, cannot improve upon the formant estimation accuracy of the first stage. The proposed TVQCP method provides a single-stage formant tracking that combines the estimation and tracking stages into one. TVQCP analysis combines three approaches to improve formant estimation and tracking: (1) it uses temporally weighted quasi-closed-phase analysis to derive closed-phase es-timates of the vocal tract with reduced interference from the excitation source, (2) it increases the residual sparsity by using the L1 optimization and (3) it uses time-varying linear prediction analysis over long time windows (e.g., 100–200 ms) to impose a continuity constraint on the vocal tract model and hence on the formant trajectories. Formant tracking experiments with a wide variety of synthetic and natural speech signals show that the proposed TVQCP method performs better than conventional and popular formant tracking tools, such as Wavesurfer and Praat (based on dynamic programming), the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on deep neural networks trained in a supervised manner).Description
Keywords
Time-varying linear prediction, weighted linear prediction, quasi-closed-phase analysis, formant tracking
Other note
Citation
Gowda, D, Kadiri, S, Story, B & Alku, P 2020, ' Time-varying quasi-closed-phase analysis for accurate formant tracking in speech signals ', IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, 9108548, pp. 1901-1914 . https://doi.org/10.1109/TASLP.2020.3000037