Unit selection provides the greatest naturalness, because it applies only a small amount of digital signal processing (DSP) to the recorded speech. DSP often makes recorded speech sound less natural, although some systems use a small amount of signal processing at the point of concatenation to smooth the waveform. The output from the best unit-selection systems is often indistinguishable from real human voices, especially in contexts for which the TTS system has been tuned. However, maximum naturalness typically require unit-selection speech databases to be very large, in some systems ranging into the gigabytes of recorded data, representing dozens of hours of speech.John Kominek and Alan W. Black. (2003). CMU ARCTIC databases for speech synthesis. CMU-LTI-03-177. Language Technologies Institute, School of Computer Science, Carnegie Mellon University. Also, unit selection algorithms have been known to select segments from a place that results in less than ideal synthesis (e.g. minor words become unclear) even when a better choice exists in the database.Julia Zhang. Language Generation and Speech Synthesis in Dialogues for Language Learning, masters thesis, Section 5.6 on page 54. Recently, researchers have proposed various automated methods to detect unnatural segments in unit-selection speech synthesis systems.William Yang Wang and Kallirroi Georgila. (2011). Automatic Detection of Unnatural Word-Level Segments in Unit-Selection Speech Synthesis, IEEE ASRU 2011.
Unit selection provides the greatest naturalness, because it applies only a small amount of digital signal processing (DSP) to the recorded speech. DSP often makes recorded speech sound less natural, although some systems use a small amount of signal processing at the point of concatenation to smooth the waveform. The output from the best unit-selection systems is often indistinguishable from real human voices, especially in contexts for which the TTS system has been tuned. However, maximum naturalness typically require unit-selection speech databa.ses to be very large, in some systems ranging into the gigabytes of recorded data, representing dozens of hours of speech.John Kominek and Alan W. Black. (2003). CMU ARCTIC databases for speech synthesis. CMU-LTI-03-177 Language Technologies Institute School of Computer Science, Carnegie Mellon University. Also, unit selection algorithms have been known to select segments from a place that results in less than ideal synthesis (e.g. minor words become unclear) even when a better choice exists in the database.Julia Zhang. Language Generation and Speech Synthesis in Dialogues for Language Learning, masters thesis, Section 5.6 on page 54. Recently, researchers have proposed various automated methods to detect unnatural segments in unit-selection speech synthesis systems.William Yang Wang and Kallirroi Georgila. (2011). Automatic Detection of Unnatural Word-Level Segments in Unit-Selection Speech Synthesis, IEEE ASRU 2011.