Acoustic and lexical resource constrained ASR using language-independent acoustic model and language-dependent probabilistic lexical model
TLDR
This paper shows that the relationship between lexical units and acoustic features can be factored into two parts through a latent variable, namely, an acoustic model and a lexical model and proposes an approach that addresses both acoustic and phonetic lexical resource constraints in ASR system development.About:
This article is published in Speech Communication.The article was published on 2015-04-01 and is currently open access. It has received 23 citations till now. The article focuses on the topics: Acoustic model & Literature survey.read more
Citations
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IEEE transactions on pattern analysis and machine intelligence
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.
Journal ArticleDOI
Regularized Speaker Adaptation of KL-HMM for Dysarthric Speech Recognition
TL;DR: A speaker adaptation method based on a combination of L2 regularization and confusion-reducing regularization, which can enhance discriminability between categorical distributions of the KL-HMM states while preserving speaker-specific information is proposed.
Journal ArticleDOI
Articulatory feature based continuous speech recognition using probabilistic lexical modeling
TL;DR: Analysis of the probabilistic relationship captured by the parameters has shown that the approach is capable of adapting the knowledge-based phoneme-to-AF representations using speech data; and allows different AFs to evolve asynchronously.
Proceedings ArticleDOI
On modeling context-dependent clustered states: Comparing HMM/GMM, hybrid HMM/ANN and KL-HMM approaches
TL;DR: It is shown that in KL-HMM framework the authors may not require as many clustered states as the best HMM/GMM system in the ANN output layer, which has broader implications on model complexity and data sparsity issues.
Journal ArticleDOI
Acoustic data-driven grapheme-to-phoneme conversion in the probabilistic lexical modeling framework
TL;DR: The recently proposed acoustic G2P approach in the Kullback Leibler divergence-based HMM (KL-HMM) framework is a particular case of this formalism, and experimental studies on English and French show that despite relatively poor performance at the pronunciation level, the performance of the proposed approach is not significantly different than the state-of-the-art G 2P methods at the ASR level.
References
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Journal ArticleDOI
A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
Journal ArticleDOI
Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This article provides an overview of progress and represents the shared views of four research groups that have had recent successes in using DNNs for acoustic modeling in speech recognition.
Journal ArticleDOI
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
TL;DR: A pre-trained deep neural network hidden Markov model (DNN-HMM) hybrid architecture that trains the DNN to produce a distribution over senones (tied triphone states) as its output that can significantly outperform the conventional context-dependent Gaussian mixture model (GMM)-HMMs.
Journal Article
Deep Neural Networks for Acoustic Modeling in Speech Recognition
Geoffrey E. Hinton,Li Deng,Dong Yu,George E. Dahl,Abdelrahman Mohamed,Navdeep Jaitly,Andrew W. Senior,Vincent Vanhoucke,Patrick Nguyen,Tara N. Sainath,Brian Kingsbury +10 more
TL;DR: This paper provides an overview of this progress and repres nts the shared views of four research groups who have had recent successes in using deep neural networks for a coustic modeling in speech recognition.
IEEE transactions on pattern analysis and machine intelligence
TL;DR: This special issue aims at gathering the recent advances in learning with shared information methods and their applications in computer vision and multimedia analysis and addressing interesting real-world computer Vision and multimedia applications.