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Top1. Introduction
There is a strong need for higher quality Text-To-Speech (TTS) conversion in broadcasting services. The development of a TTS system that can generate synthesized speech that sounds similar to a human voice could improve access to text information in both data broadcasts (Sakai, 2007) and broadband contents (Baba, 2012) for visually impaired and mobile receivers. Moreover, a high-quality TTS system could facilitate the development of automatic spoken broadcasts, such as weather reports (Segi, 2013) and even automatic television broadcasts, by combining speech with computer-graphic animations generated from a script (Hayashi, 2013; Doke, 2012).
Several types of TTS system have been reported to date. One group utilizes the compilation of recorded speech sounds, which is employed in airport and train announcements (Demeur, 1987). Although the speech synthesized by this method has not yet been evaluated, it is widely considered to achieve human voice quality based on its use in broadcast systems. However, the content of the speech synthesized by this method is limited to combinations of recorded phrases connected by silent sections. Thus, it cannot be utilized for the speech synthesis of arbitrary input sentences. Moreover, this method does not take coarticulation into account, suggesting that the naturalness of the synthesized speech is degraded without sufficent silence sections. Indeed, 91% of synthesized speech with coarticulation was evaluated as more natural than synthesized speech without coarticulation in a previous study (Segi, 2010).
A second group of TTS systems employs Hidden Markov Models (HMMs) (Zen, 2009; Toda, 2007). This method analyzes the speech data, extracts prosody and voice-quality components from the speech data respectively, and allows them to be controlled independently (Kawahara, 1999). For example, HMM TTS systems can extract the feature parameters of phoneme “a” from a speech database, and use them to synthesize speech. This method has several advantages as it is easy to use for voice conversion, has good performance with small speech databases, and does not require a high-performance Central Processing Unit (CPU) or large memory. However, the naturalness of the speech synthesized using this method is not so high (Zen, 2008; Takaki, 2011; Nose, 2013).