A neuronal formant synthesizer

Speech synthesis by rule has made considerable advances and it is being used today in numerous text-to-speech synthesis systems. Current systems are able to synthesise pleasant-sounding voices at high intelligibility levels. However, because their synthetic speech quality is still inferior to that of fluently produced human speech it has not found wide acceptance and instead it has been restricted mainly in useful applications for the handicapped or for restricted tasks in telecommunications. The problems with automatic speech synthesis are related to the methods of controlling speech synthesizer models in order to mimic the varying properties of the human speech production system during discourse. In this paper, artificial neural networks are developed for the control of a formant synthesizer. A set of common words comprising of larynx-produced phonemes were analysed and used to train a neural network cluster. The system was able to produce intelligible speech for certain phonemic combinations of new and unfamiliar words.