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Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition
Type of publication: Thesis
Citation: Pinto_THESIS_2010
Year: 2010
School: Ecole polytechnique fédérale de Lausanne
Note: Thèse Ecole polytechnique fédérale de Lausanne EPFL, no 4649 (2010,',','), Programme doctoral Génie électrique, Faculté des sciences et techniques de l'ingénieur STI, Institut de génie électrique et électronique IEL (Laboratoire de l'IDIAP LIDIAP). Dir.: Hervé Bourlard
Abstract: In this thesis, we investigate a hierarchical approach for estimating the phonetic class-conditional probabilities using a multilayer perceptron (MLP) neural network. The architecture consists of two MLP classifiers in cascade. The first MLP is trained in the conventional way using standard acoustic features with a temporal context of around 90 ms. The second MLP is trained on the phonetic class-conditional probabilities (or posterior features) estimated by the first classifier, but with a relatively longer temporal context of around 150-250 ms. The hierarchical architecture is motivated towards exploiting the useful contextual information in the sequence of posterior features which includes the evolution of the probability values within a phoneme (sub-phonemic) and its transition to/from neighboring phonemes (sub-lexical). As the posterior features are sparse and simple, the second classifier is able to learn the contextual information spanning a context as long as 250 ms. Extensive experiments on the recognition of phonemes on read speech as well as conversational speech show that the hierarchical approach yields significantly higher recognition accuracies. Analysis of the second MLP classifier using Volterra series reveal that it has learned the phonetic-temporal patterns in the posterior feature space which captures the confusions in phoneme classification at the output of the first classifier as well as the phonotactics of the language as observed in the training data. Furthermore, we show that the second MLP can be simple in terms of the number of model parameters and that it can be trained on lesser training data. The usefulness of the proposed hierarchical acoustic modeling in automatic speech recognition (ASR) is demonstrated using two applications (a) task adaptation where the goal is to exploit MLPs trained on large amount of data and available off-the-shelf to new tasks and (b) large vocabulary continuous ASR on broadcast news and broadcast conversations in Mandarin. Small vocabulary isolated word recognition and task adaptation studies are performed on the Phonebook database and the large vocabulary speech recognition studies are performed on the DARPA GALE Mandarin database.
Projects Idiap
Authors Pinto, Joel Praveen
Added by: [UNK]
Total mark: 0
  • Pinto_THESIS_2010.pdf