The synthesizer uses a variant of linear predictive coding and has a small in-built vocabulary. More recent synthesizers, developed by Jorge. " TI will exit dedicated speech-synthesis chips, transfer products to Sensory Archived at WebCite." June 14, 2001. This is similar to the "sounding out or synthetic phonics, approach to learning reading.
The other approach is rule-based, in which pronunciation rules are applied to words to determine their pronunciations based on their spellings. A b Lucero,. Noriko Umeda. Org. However, in practice, neural vocoder can generalize well even when the input features are more smooth than real data. Using the Votrax SC01 chip in 1983.
Penguin Books. Vol. . Small footprint Despite the many advantages mentioned, end-to-end methods still have many challenges to be solved: Auto-regressive-based models suffer from slow inference problem Output speech are not robust when data are not sufficient Lack of controllability compared with traditional concatenative. This shows the community that only using a single model to generate speech of multiple style is possible. In certain systems, this part includes the computation of the target prosody (pitch contour, phoneme durations 4 which is then imposed on the output speech.