Abstract: In accordance with a present invention speech training system is disclosed. It uses a microphone to receive audible sounds input by a user into a first computing device having a program with a database consisting of (i) digital representations of known audible sounds and associated alphanumeric representations of the known audible sounds, and (ii) digital representations of known audible sounds corresponding to mispronunciations resulting from known classes of mispronounced words and phrases. The method is performed by receiving the audible sounds in the form of the electrical output of the microphone. A particular audible sound to be recognized is converted into a digital representation of the audible sound. The digital representation of the particular audible sound is then compared to the digital representations of the known audible sounds to determine which of those known audible sounds is most likely to be the particular audible sound being compared to the sounds in the database.

Abstract: A preferred embodiment of the method for converting text to speech using a computing device having a memory is disclosed. Text, being made up of a plurality of words, is received into the memory of the computing device. A plurality of phonemes are derived from the text. Each of the phonemes is associated with a prosody record based on a database of prosody records associated with a plurality of words. A first set of the artificial intelligence rules is applied to determine context information associated with the text. The context influenced prosody changes for each of the phonemes is determined. Then a second set of rules, based on Lessac theory to determine Lessac derived prosody changes for each of the phonemes is applied. The prosody record for each of the phonemes is amended in response to the context influenced prosody changes and the Lessac derived prosody changes. Then a reading from the memory sound information associated with the phonemes is performed.