Publication

On Joint Modelling of Grapheme and Phoneme Information using KL-HMM for ASR

Hervé Bourlard, Mathew Magimai Doss
2009
Reports

Abstract

In this paper, we propose a simple approach to jointly model both grapheme and phoneme information using Kullback-Leibler divergence based HMM (KL-HMM) system. More specifically, graphemes are used as subword units and phoneme posterior probabilities estimated at output of multilayer perceptron are used as observation feature vector. Through preliminary studies on DARPA Resource Management corpus it is shown that although the proposed approach yield lower performance compared to KL-HMM system using phoneme as subword units, this gap in the performance can be bridged via temporal modelling at the observation feature vector level and contextual modelling of early tagged contextual graphemes.

Official source

https://infoscience.epfl.ch/entities/publication/1e259456-e8a9-4b3b-b1d3-7e7f3dfa3a92

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Hervé Bourlard, Mathew Magimai Doss
2009
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/1e259456-e8a9-4b3b-b1d3-7e7f3dfa3a92

About this result

Related concepts (32)

Multilayer perceptron

A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not linearly separable. It is a misnomer because the original perceptron used a Heaviside step function, instead of a nonlinear kind of activation function (used by modern networks).

Phonemic orthography

A phonemic orthography is an orthography (system for writing a language) in which the graphemes (written symbols) correspond to the phonemes (significant spoken sounds) of the language. Natural languages rarely have perfectly phonemic orthographies; a high degree of grapheme–phoneme correspondence can be expected in orthographies based on alphabetic writing systems, but they differ in how complete this correspondence is.

Perceptrons (book)

Perceptrons: an introduction to computational geometry is a book written by Marvin Minsky and Seymour Papert and published in 1969. An edition with handwritten corrections and additions was released in the early 1970s. An expanded edition was further published in 1987, containing a chapter dedicated to counter the criticisms made of it in the 1980s. The main subject of the book is the perceptron, a type of artificial neural network developed in the late 1950s and early 1960s.

Related publications (28)

On Joint Modelling of Grapheme and Phoneme Information using KL-HMM for ASR

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