By Daniel Vasquez, Rainer Gruhn, Wolfgang Minker
In this ebook, hierarchical buildings in response to neural networks are investigated for automated speech popularity. those constructions are in general evaluated in the phoneme acceptance job below the Hybrid Hidden Markov Model/Artificial Neural community (HMM/ANN) paradigm. The baseline hierarchical scheme involves degrees every one that's in response to a Multilayered Perceptron (MLP). also, the output of the 1st point is used as an enter for the second one point. the program may be considerably accelerated through elimination the redundant details contained on the output of the 1st level.
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Additional info for Hierarchical Neural Network Structures for Phoneme Recognition
GMM with diagonal covariance matrices. – Duration modeled by gamma distribution per state. – MFCC + Delta + double Delta and trigram model. 9* – Hybrid HMM/ANN paradigm. – ANN topology based on RNN. – RNN as an alternative to exploit large context information. 3 – HMM/GMM with parameter tying. – Motivation to ﬁnd a good compromise between number of parameters and amount of training data. – Use of the publically available HTK toolkit3 . 5* – Segment-based decoder, representing speech as a graph.
Each syllable is characterized by their correspondent phoneme sequence. Each language has its own constraints in the way vowels and consonants are combined into syllables. These constraints are known as the phonotactics of the language. Syllables are grouped together to form spoken words. Therefore, syllables are also considered as intermediate units between phonemes and words. Similar to syllables, which are distinguished among them based on their phoneme combination, words can be distinguished based on their syllable level structure.
In particular, techniques involving neural networks are emphasized. A simple and successful approach based on a sequential concatenation of two MLPs ([Pinto 08b]) is described in detail in the next chapter, since it forms the basis of this book. 1 Tandem Approach In [Hermansky 00], the authors proposed a method for combining a discriminative and a generative model in tandem. This method can be classiﬁed as 42 3 Phoneme Recognition Task a hierarchical structure where the output of a ﬁrst level classiﬁer based on neural networks are postprocessing and then sent to a second level classiﬁer based on HMM/GMM.
Hierarchical Neural Network Structures for Phoneme Recognition by Daniel Vasquez, Rainer Gruhn, Wolfgang Minker