{"id":155178,"date":"2006-03-01T00:00:00","date_gmt":"2006-03-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/tracking-vocal-tract-resonances-using-a-quantized-nonlinear-function-embedded-in-a-temporal-constraint\/"},"modified":"2018-10-16T20:17:41","modified_gmt":"2018-10-17T03:17:41","slug":"tracking-vocal-tract-resonances-using-a-quantized-nonlinear-function-embedded-in-a-temporal-constraint","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/tracking-vocal-tract-resonances-using-a-quantized-nonlinear-function-embedded-in-a-temporal-constraint\/","title":{"rendered":"Tracking Vocal Tract Resonances Using a Quantized Nonlinear Function Embedded in a Temporal Constraint"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a new technique for high-accuracy<br \/>\ntracking of vocal-tract resonances (which coincide with<br \/>\nformants for nonnasalized vowels) in natural speech. The technique<br \/>\nis based on a discretized nonlinear prediction function,<br \/>\nwhich is embedded in a temporal constraint on the quantized<br \/>\ninput values over adjacent time frames as the prior knowledge for<br \/>\ntheir temporal behavior. The nonlinear prediction is constructed,<br \/>\nbased on its analytical form derived in detail in this paper, as<br \/>\na parameter-free, discrete mapping function that approximates<br \/>\nthe \u201cforward\u201d relationship from the resonance frequencies and<br \/>\nbandwidths to the Linear Predictive Coding (LPC) cepstra of<br \/>\nreal speech. Discretization of the function permits the \u201cinversion\u201d<br \/>\nof the function via a search operation. We further introduce the<br \/>\nnonlinear-prediction residual, characterized by a multivariate<br \/>\nGaussian vector with trainable mean vectors and covariance<br \/>\nmatrices, to account for the errors due to the functional approximation.<br \/>\nWe develop and describe an expectation\u2013maximization<br \/>\n(EM)-based algorithm for training the parameters of the residual,<br \/>\nand a dynamic programming-based algorithm for resonance<br \/>\ntracking. Details of the algorithm implementation for computation<br \/>\nspeedup are provided. Experimental results are presented which<br \/>\ndemonstrate the effectiveness of our new paradigm for tracking<br \/>\nvocal-tract resonances. In particular, we show the effectiveness of<br \/>\ntraining the prediction-residual parameters in obtaining high-accuracy<br \/>\nresonance estimates, especially during consonantal closure.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a new technique for high-accuracy tracking of vocal-tract resonances (which coincide with formants for nonnasalized vowels) in natural speech. The technique is based on a discretized nonlinear prediction function, which is embedded in a temporal constraint on the quantized input values over adjacent time frames as the prior knowledge for their temporal [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"deng"},{"type":"user_nicename","value":"alexac"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"IEEE Trans. on Audio, Speech and Language Processing","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"2","msr_journal":"IEEE Trans. on Audio, Speech and Language 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