1. A method comprising: detecting, via a machine learning algorithm modeling human perception and trained with acoustic parameters from each syllable in a word, incorrect stress patterns in selected acoustic units representing speech to be synthesized, wherein the selected acoustic units comprise phonemes and come from a database of energy-normalized acoustic units that are normalized on a sentence basis; performing a word level analysis of the incorrect stress patterns, a phrase level analysis of the incorrect stress patterns and a sentence level analysis of the incorrect stress patterns to yield analyses, wherein the analyses are performed in series; and modifying, via a processor and prior to waveform synthesis, the incorrect stress patterns in the selected acoustic units according to the analyses, to yield corrected stress patterns.
1. A method comprising: detecting, via a machine learning algorithm modeling human perception and trained with acoustic parameters from each syllable in a word, incorrect stress patterns in selected acoustic units representing speech to be synthesized, wherein the selected acoustic units comprise phonemes and come from a database of energy-normalized acoustic units that are normalized on a sentence basis; performing a word level analysis of the incorrect stress patterns, a phrase level analysis of the incorrect stress patterns and a sentence level analysis of the incorrect stress patterns to yield analyses, wherein the analyses are performed in series; and modifying, via a processor and prior to waveform synthesis, the incorrect stress patterns in the selected acoustic units according to the analyses, to yield corrected stress patterns. 4. The method of claim 1 , further comprising receiving a stress pattern for both a language and an accent in the language, wherein the detecting of the incorrect stress patterns is performed based on the stress pattern.