Inverting mappings from smooth paths through Rn to paths through Rm: A technique applied to recovering articulation from acoustics

Hogden, John; Rubin, Philip E.; McDermott, Erik; Katagiri, Shigeru; Goldstein, Louis

doi:10.1016/j.specom.2007.02.008

Cited by 12 publications

(6 citation statements)

References 63 publications

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“…Therefore, it is necessary to impose some assumptions on either the generation of the data or to restrict the class of non-linear functions, in order to obtain a unique solution. For example, articulation from acoustics is recovered in [7] by assuming that the unobserved signals of articulator motion are band-pass. Here we assume that the data is generated by stochastic Itô processes.…”

Section: Introductionmentioning

confidence: 99%

Non-linear independent component analysis with diffusion maps

Singer

Coifman

2008

Applied and Computational Harmonic Analysis

145

226

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We introduce intrinsic, non-linearly invariant, parameterizations of empirical data, generated by a non-linear transformation of independent variables. This is achieved through anisotropic diffusion kernels on observable data manifolds that approximate a Laplacian on the inaccessible independent variable domain. The key idea is a symmetrized second-order approximation of the unknown distances in the independent variable domain, using the metric distortion induced by the Jacobian of the unknown mapping from variables to data. This distortion is estimated using local principal component analysis. Thus, the non-linear independent component analysis problem is solved whenever the generation of the data enables the estimation of the Jacobian. In particular, we obtain the non-linear independent components of stochastic Itô processes and indicate other possible applications.

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Section: Introductionmentioning

confidence: 99%

Non-linear independent component analysis with diffusion maps

Singer

Coifman

2008

Applied and Computational Harmonic Analysis

145

226

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“…Thus, one of the goals of this study is to quantify the sensitivity of acoustic parameters to changes in articulatory parameters. This is an important consideration in phonetics, as, for instance, in Stevens' quantal theory ͑Stevens, 1972, 1989Wood, 1979͒. Another major area of research in the speech sciences is in the speech inverse problem: inferring articulatory information from speech acoustics in an algorithmic manner ͑e.g., Atal et al, 1978;McGowan and Cushing, 1999;Hogden et al, 2007͒. The inverse mappings derived in the present work provide a data-driven model to predict acoustics from articulation.…”

Section: Introductionmentioning

confidence: 72%

Acoustic-articulatory mapping in vowels by locally weighted regression

McGowan¹,

Berger

2009

The Journal of the Acoustical Society of America

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A method for mapping between simultaneously measured articulatory and acoustic data is proposed. The method uses principal components analysis on the articulatory and acoustic variables, and mapping between the domains by locally weighted linear regression, or loess [Cleveland, W. S. (1979). J. Am. Stat. Assoc. 74, 829-836]. The latter method permits local variation in the slopes of the linear regression, assuming that the function being approximated is smooth. The methodology is applied to vowels of four speakers in the Wisconsin X-ray Microbeam Speech Production Database, with formant analysis. Results are examined in terms of (1) examples of forward (articulation-to-acoustics) mappings and inverse mappings, (2) distributions of local slopes and constants, (3) examples of correlations among slopes and constants, (4) root-mean-square error, and (5) sensitivity of formant frequencies to articulatory change. It is shown that the results are qualitatively correct and that loess performs better than global regression. The forward mappings show different root-mean-square error properties than the inverse mappings indicating that this method is better suited for the forward mappings than the inverse mappings, at least for the data chosen for the current study. Some preliminary results on sensitivity of the first two formant frequencies to the two most important articulatory principal components are presented.

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“…Toda et al [3] achieved almost identical results on the same data by applying expectationmaximization using both minimum mean-squared error and maximum likelihood estimation to a Gaussian mixture mapping function with low-pass filtering. Simpler approaches achieved similar results (errors less than 2mm, typically around 1mm) using simple vector quantization with an appropriate number of vectors [4].…”

Section: Introductionmentioning

confidence: 82%

Adaptive kernel canonical correlation analysis for estimation of task dynamics from acoustics

Rudzicz¹

2010

2010 IEEE International Conference on Acoustics, Speech and Signal Processing

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We present a method for acoustic-articulatory inversion whose targets are the abstract tract variables from task dynamic theory. Towards this end we construct a non-linear Hammerstein system whose parameters are updated with adaptive kernel canonical correlation analysis. This approach is notably semi-analytical and applicable to large sets of data. Training behaviour is compared across four kernel functions and prediction of tract variables is shown to be significantly more accurate than state-of-the-art mixture density networks.

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Inverting mappings from smooth paths through Rn to paths through Rm: A technique applied to recovering articulation from acoustics

Cited by 12 publications

References 63 publications

Non-linear independent component analysis with diffusion maps

Non-linear independent component analysis with diffusion maps

Acoustic-articulatory mapping in vowels by locally weighted regression

Adaptive kernel canonical correlation analysis for estimation of task dynamics from acoustics

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