Statistics – Applications
Scientific paper
2009-11-09
IEEE Transactions on Audio, Speech, and Language Processing, vol. 19, pp. 977-989, 2011
Statistics
Applications
12 pages, 12 figures; revised version
Scientific paper
10.1109/TASL.2010.2073704
This article develops a general detection theory for speech analysis based on time-varying autoregressive models, which themselves generalize the classical linear predictive speech analysis framework. This theory leads to a computationally efficient decision-theoretic procedure that may be applied to detect the presence of vocal tract variation in speech waveform data. A corresponding generalized likelihood ratio test is derived and studied both empirically for short data records, using formant-like synthetic examples, and asymptotically, leading to constant false alarm rate hypothesis tests for changes in vocal tract configuration. Two in-depth case studies then serve to illustrate the practical efficacy of this procedure across different time scales of speech dynamics: first, the detection of formant changes on the scale of tens of milliseconds of data, and second, the identification of glottal opening and closing instants on time scales below ten milliseconds.
Quatieri Thomas F.
Rudoy Daniel
Wolfe Patrick J.
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