Sparse Multilayer Perceptron for Phoneme Recognition

By Garimella S. V. S. Sivaram, Hynek Hermansky. 2012

63 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Signal Processing

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Citations in the corpus

A tutorial survey of architectures, algorithms, and applications for deep learning (2014, methodology): “Recently, the deep learning technique was successfully applied to phone recognition [23, 24, 116, 126, 165, 166] and large vocabulary speech recognition tasks [22, 25, 135, 155, 156, 167–169] by integrating the powerful discriminative training ability of DNNs with the sequential modeling ability of ”
Improving low-resource CD-DNN-HMM using dropout and multilingual DNN training (2013, methodology): “To alleviate the effects of data sparseness, attempts have been made to build sparse DNN, either through imposing regularizers on hidden-layer parameters [4, 6] or through rounding close-to-zero parameters back to zero [4, 7].”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, methodology): “In a further study reported in [232], [267], [268], in deriving sparse feature representations for speech, an auto-associative neural network is used, whose internal hidden-layer output is constrained to be sparse.”
Evaluating speech features with the minimal-pair ABX task: analysis of the classical MFC/PLP pipeline (2013, background): “Here, we extend and complement the framework proposed by Carlin, Jansen and Hermansky [1] for the evaluation of speech features in zero resource settings.”
Acoustic Modeling With Hierarchical Reservoirs (2013, background): “In line with this reasoning and in analogy with what is proposed in [17], [18], we consider entire phones as the acoustic units to distinguish in the second layer.”
Deep learning for robust feature generation in audiovisual emotion recognition (2013, methodology): “Deep learning techniques (See [9] for a survey) have become increasingly popular in various communities including speech and language processing [10–12] and vision processing [26–30].”
An Overview of Deep-Structured Learning for Information Processing (2011, methodology): “Recently, the deep learning technique was successfully applied to phone recognition (Mohamed et al., 2009, 2010, 2012; Sivaram and Hermansky, 2012) and large vocabulary speech recognition tasks (Yu et al., 2012) by integrating the powerful discriminative training ability of the DBNs and the…”