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    Time-varying quasi-closed-phase analysis for accurate formant tracking in speech signals

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    Name:
    Time_varying_quasi_closed_phas ...
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    Description:
    Final Accepted Manuscript
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    Author
    Gowda, Dhananjaya
    Kadiri, Sudarsana Reddy
    Story, Brad
    Alku, Paavo
    Affiliation
    Univ Arizona
    Issue Date
    2020
    Keywords
    Time-varying linear prediction
    weighted linear prediction
    quasi-closed-phase analysis
    formant tracking
    
    Metadata
    Show full item record
    Publisher
    IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
    Citation
    D. Gowda, S. R. Kadiri, B. Story and P. Alku, "Time-Varying Quasi-Closed-Phase Analysis for Accurate Formant Tracking in Speech Signals," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 28, pp. 1901-1914, 2020, doi: 10.1109/TASLP.2020.3000037.
    Journal
    IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
    Rights
    © 2020 IEEE.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    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. Conventional formant tracking methods typically adopt a two-stage estimateand-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 he, 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 estimates of the vocal tract with reduced interference from the excitation source, (2) it increases the residual sparsity by using the L-1 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). Matlab scripts for the proposed method can be found at: https://github.com/njaygowda/ftrack
    ISSN
    2329-9290
    EISSN
    2329-9304
    DOI
    10.1109/taslp.2020.3000037
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1109/taslp.2020.3000037
    Scopus Count
    Collections
    UA Faculty Publications

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