Download Text-To-Speech Technology-Based Programming Tool Final Doc PDF

TitleText-To-Speech Technology-Based Programming Tool Final Doc
TagsHuman–Computer Interaction Oral Communication Human Voice Speech Speech Synthesis
File Size839.8 KB
Total Pages57
Document Text Contents
Page 28

Text-to-Speech Technology-Based Programming ToolText-to-Speech Technology-Based Programming Tool

2828

The system, first marketed in 1994, provides full articulatory-based text-to-The system, first marketed in 1994, provides full articulatory-based text-to-

speech conversion using a waveguide or transmission-line analog of the humanspeech conversion using a waveguide or transmission-line analog of the human

oral and nasal tracts controlled by Carré's "distinctive region model".oral and nasal tracts controlled by Carré's "distinctive region model".

HMMHMM-based synthesis-based synthesis

HMM-based synthesis is a synthesis method based on hidden Markov models,HMM-based synthesis is a synthesis method based on hidden Markov models,

also called Statistical Parametric Synthesis. In this system, the frequencyalso called Statistical Parametric Synthesis. In this system, the frequency

spectrum (vocal tract), fundamental frequency(vocal source), and durationspectrum (vocal tract), fundamental frequency(vocal source), and duration

(prosody) of speech are modeled simultaneously by HMMs.(prosody) of speech are modeled simultaneously by HMMs.

Speech waveforms are generated from HMMs themselves based onSpeech waveforms are generated from HMMs themselves based on

the maximum likelihood criterion.the maximum likelihood criterion.
[27][27]

Sine wave synthesisSine wave synthesis

Sine wave synthesis is a technique for synthesizing speech by replacingSine wave synthesis is a technique for synthesizing speech by replacing

the formants (main bands of energy) with pure tone whistles.the formants (main bands of energy) with pure tone whistles.
[28][28]

ChallengesChallenges

Text normalization challengesText normalization challenges

The process of normalizing text is rarely straightforward. Texts are fullThe process of normalizing text is rarely straightforward. Texts are full

of heteronyms, numbers, and abbreviations that all require expansion into aof heteronyms, numbers, and abbreviations that all require expansion into a

phonetic representation.phonetic representation.

Similer Documents