Publication

Posterior Features for Template-based ASR

Hervé Bourlard, Mathew Magimai Doss
2011
Conference Papers

Abstract

This paper investigates the use of phoneme class conditional probabilities as features (posterior features) for template-based ASR. Using 75 words and 600 words task-independent and speaker-independent setup on Phonebook database, we investigate the use of different posterior distribution estimators, different distance measures that are better suited for posterior distributions, and different training data. The reported experiments clearly demonstrate that posterior features are always superior, and generalize better than other classical acoustic features (at the cost of training a posterior distribution estimator).

Official source

https://infoscience.epfl.ch/entities/publication/e36c2690-bfba-423f-9d1f-cc1e768c29ea

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Hervé Bourlard, Mathew Magimai Doss
2011
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e36c2690-bfba-423f-9d1f-cc1e768c29ea

About this result

Related concepts (32)

Bayes estimator

In estimation theory and decision theory, a Bayes estimator or a Bayes action is an estimator or decision rule that minimizes the posterior expected value of a loss function (i.e., the posterior expected loss). Equivalently, it maximizes the posterior expectation of a utility function. An alternative way of formulating an estimator within Bayesian statistics is maximum a posteriori estimation. Suppose an unknown parameter is known to have a prior distribution .

Maximum a posteriori estimation

In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution. The MAP can be used to obtain a point estimate of an unobserved quantity on the basis of empirical data. It is closely related to the method of maximum likelihood (ML) estimation, but employs an augmented optimization objective which incorporates a prior distribution (that quantifies the additional information available through prior knowledge of a related event) over the quantity one wants to estimate.

Geometric distribution

In probability theory and statistics, the geometric distribution is either one of two discrete probability distributions: The probability distribution of the number X of Bernoulli trials needed to get one success, supported on the set ; The probability distribution of the number Y = X − 1 of failures before the first success, supported on the set . Which of these is called the geometric distribution is a matter of convention and convenience. These two different geometric distributions should not be confused with each other.

Related publications (3)

Posterior Features for Template-based ASR

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WASP: Scalable Bayes via barycenters of subset posteriors

MATHICSE Technical Report : A posteriori error estimations for elliptic partial differential equations with small uncertainties

Synthetic References for Template-based ASR using Posterior Features