Voice synthesis employs machine voice to generate synthetic speech. Not to be confused with voice generation, which encompasses all forms of producing spoken words, voice synthesis specifically refers to machine-generated speech. Voice synthesis is rooted in the development of speech synthesis and speech recognition, two areas of research dedicated, respectively, to the generation and comprehension of human speech by machines. By understanding the fundamental elements of synthesis Chinese, Russian, or Czech languages, different types of artificial voices such as Esperantino, Hiragana, or other phonetic varieties are created, as well as the parallels among humans and machines when memorizing, developing, and producing sentences normally: pitch control, expression control, and rhythmic pronunciation and intonation. Many of these properties are also used to model human voice in speech synthesis systems.
The performance of Text-To-Speech (TTS) systems is chiefly assessed through their perceived naturalness and expressiveness. Naturalness encompasses the degree to which synthetic speech seems human, whereas expressiveness conveys the extent to which the delivery is appropriate to the situational context. Five acoustic features have a central role in naturalness: the spectrum, prosody, formant frequencies, spectral speeds of variation, and voice quality; perceived quality and the ability for meaningful rhyme can also affect the naturalness. Despite the apparent separation of the three systems in the TTS pipeline—content, prosody, and spectral variation—there is no reason to treat them independently since it has been shown experimentally that prosody and spectral variation interact with each other.
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