Voice-customizable text-to-speech for intelligent home-care system

In the home care scenarios, the feedback voices are often presented in reading style (neutral); however, users prefer known voices, such as those of family members, friends or celebrities. For this purpose, the speaker adaptation or voice conversion is usually adopted. However, these techniques demand a well-tagged speaker-dependent or parallel corpus, and building such a corpus is a time-consuming task. Moreover, users don´t have the ability to deal with such a database by themselves. Therefore, this study presents a voice-customizable text-to-speech system which allows the system voice can be changed easily by users. In this system, parallel corpus generation, voice conversion based on decision tree and linear multivariate regression (LMR), and HMM-based speech synthesis are integrated. The advantage of this system is that the target speaker needs to record only a small amount of speech data according to a pre-designed balanced text corpus. Thus, the voice conversion models can be automatically estimated and stored. In subjective and objective evaluations, the operation convenience or friendliness of the proposed system is better than the baseline system.