By Wolfgang Minker, Daniel Vasquez, Rainer Gruhn
During this ebook, hierarchical constructions in keeping with neural networks are investigated for automated speech attractiveness. those constructions are quite often evaluated in the phoneme popularity job less than the Hybrid Hidden Markov Model/Artificial Neural community (HMM/ANN) paradigm. The baseline hierarchical scheme includes degrees each one that is 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 by means of elimination the redundant details contained on the output of the 1st point.
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Additional info for Hierarchical Neural Network Structures for Phoneme Recognition (Signals and Communication Technology)
Phonetics links to phonology via a mapping from acoustic signal, articulation and sound perception to the abstract speech units such as phonemes or syllables. In speech science, a phoneme is deﬁned as the minimal unit of speech sound in a language that can serve to distinguish meaning. Phonemes are in fact categories, where each category is characterized by a prototype [Gasser 06]. Instances of each category, represented as individual sounds, are deﬁned as phones 1 . For example, the words tore and store have two diﬀerent phones or realizations of the same English phoneme /t/.
2 German Database Some experiments reported in this book have been carried out on a German database. This database is not publicly available, given that our work has been accomplished in a private company. The main reason for evaluating the proposed approaches on this database is to test if the methods are robust enough for product-level application, and to see if good results can be achieved for another language as well. This book reports the evaluation of the proposed schemes using this database as stand-alone phonetic recognizer.
1 but measured at the word level. 4 Survey TIMIT Phoneme Recognition The TIMIT database is a well-known available database which is phonetically hand-labeled. It has been published in 1987 ([Lamel 87]) and since then, many researchers have been making use of it, mainly for the task of phoneme recognition and classiﬁcation, becoming a standard database for these tasks. g. [Halberstadt 98, Deng 07, Schwarz 08, Robinson 94, Glass 03, Fernandez 08]). However, as it is mentioned in [Schwarz 08], a direct comparison of the accuracy results obtained by the diﬀerent methods is in many cases not consistent, given several variations for performing the same task.