A speech synthesizer based on an artificial neural network (ANN) is being developed for application to deeply embedded systems for language-independent speech commands on hands-free interfaces. A feed-forward, backpropagation, artificial neural network has been trained for this purpose using a custom-developed, regular expression-based, text-to-phone transcription engine to generate training patterns. Initial experimental results show the expected properties of language independence and in-system learning capability of this approach. The ANN demonstrates the capacity to generalize and map the words missing at training time, as well as to reduce contradictions related to different pronunciations for the same word.

Language-independent, neural network-based, text-to-phones conversion / M.Malcangi, D.Frontini. - In: NEUROCOMPUTING. - ISSN 0925-2312. - 73:1-3(2009 Dec), pp. 87-96. [10.1016/j.neucom.2008.08.023]

Language-independent, neural network-based, text-to-phones conversion

M. Malcangi
Primo
;
2009

Abstract

A speech synthesizer based on an artificial neural network (ANN) is being developed for application to deeply embedded systems for language-independent speech commands on hands-free interfaces. A feed-forward, backpropagation, artificial neural network has been trained for this purpose using a custom-developed, regular expression-based, text-to-phone transcription engine to generate training patterns. Initial experimental results show the expected properties of language independence and in-system learning capability of this approach. The ANN demonstrates the capacity to generalize and map the words missing at training time, as well as to reduce contradictions related to different pronunciations for the same word.
Rule-based ANN training; Speech synthesis; Text-to-speech; Window alignment
Settore INF/01 - Informatica
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/72728
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