Parametric subspace modelling of speech transitions

Reinhard, K.; Niranjan, Mahesan

doi:10.1109/icassp.1998.675462

Cited by 4 publications

(1 citation statement)

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“…Singh et al [6] used principal curves to extract skeletal structures of handwritten characters in faded documents. Reinhard and Niranjan [7] applied principal curves to model the short time spectrum of speech signals. They found that principal curves can be used efficiently to capture transitional information between phones.…”

Section: Introductionmentioning

confidence: 99%

Learning and design of principal curves

Kégl

Krzyżak

Linder

et al. 2000

IEEE Trans. Pattern Anal. Machine Intell.

302

280

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AbstractÐPrincipal curves have been defined as ªself-consistentº smooth curves which pass through the ªmiddleº of a d-dimensional probability distribution or data cloud. They give a summary of the data and also serve as an efficient feature extraction tool. We take a new approach by defining principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution. The new definition makes it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction. Our theoretical learning scheme chooses a curve from a class of polygonal lines with k segments and with a given total length to minimize the average squared distance over n training points drawn independently. Convergence properties of this learning scheme are analyzed and a practical version of this theoretical algorithm is implemented. In each iteration of the algorithm, a new vertex is added to the polygonal line and the positions of the vertices are updated so that they minimize a penalized squared distance criterion. Simulation results demonstrate that the new algorithm compares favorably with previous methods, both in terms of performance and computational complexity, and is more robust to varying data models.

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Section: Introductionmentioning

confidence: 99%

Learning and design of principal curves

Kégl

Krzyżak

Linder

et al. 2000

IEEE Trans. Pattern Anal. Machine Intell.

302

280

View full text Add to dashboard Cite

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Diphone multi-trajectory subspace models

Reinhard

Niranjan

1999

1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)

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In this paper we report on the extension of capturing speech transitions embedded in diphones using trajectory models. The slowly varying dynamics of spectral trajectories carry much discriminant information that is very crudely modelled by traditional approaches such as HMMs. We improved our methodology of explicitly capturing the trajectory of short time spectral parameter vectors introducing multi-trajectory concepts in a probabilistic framework. Optimal subspace selection is presented which finds the most discriminant plane for classification. Using the E-set from the TIMIT database results suggest that discriminant information is preserved in the subspace.

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