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• W2108460987 abstract "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." @default.
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• W2108460987 date "2011-05-01" @default.
• W2108460987 modified "2023-09-23" @default.
• W2108460987 title "Time-Varying Autoregressions in Speech: Detection Theory and Applications" @default.
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• W2108460987 doi "https://doi.org/10.1109/tasl.2010.2073704" @default.
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