Text-To-Speech Conversion with Neural Networks: A Recurrent TDNN Approach
<|reference_start|>Text-To-Speech Conversion with Neural Networks: A Recurrent TDNN Approach: This paper describes the design of a neural network that performs the phonetic-to-acoustic mapping in a speech synthesis system. The use of a time-domain neural network architecture limits discontinuities that occur at phone boundaries. Recurrent data input also helps smooth the output parameter tracks. Independent testing has demonstrated that the voice quality produced by this system compares favorably with speech from existing commercial text-to-speech systems.<|reference_end|>
@article{karaali1998text-to-speech,
    title={Text-To-Speech Conversion with Neural Networks: A Recurrent TDNN
  Approach},
    author={Orhan Karaali, Gerald Corrigan, Ira Gerson and Noel Massey},
    journal={Proceedings of Eurospeech (1997) 561-564. Rhodes, Greece},
    year={1998},
    archivePrefix={arXiv},
    eprint={cs/9811032},
    primaryClass={cs.NE cs.HC}
}