| Paper ID | SPE-34.3 |
| Paper Title |
A NEURAL TEXT-TO-SPEECH MODEL UTILIZING BROADCAST DATA MIXED WITH BACKGROUND MUSIC |
| Authors |
Hanbin Bae, Jae-Sung Bae, Young-Sun Joo, Young-Ik Kim, Hoon-Young Cho, NCSOFT, South Korea |
| Session | SPE-34: Speech Synthesis 6: Data Augmentation & Adaptation |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation Time: | Thursday, 10 June, 13:00 - 13:45 |
| Presentation |
Poster
|
| Topic |
Speech Processing: [SPE-SYNT] Speech Synthesis and Generation |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Recently, it has become easier to obtain speech data from various media such as the internet or YouTube, but directly utilizing them to train a neural text-to-speech (TTS) model is difficult. The proportion of clean speech is insufficient and the remainder includes background music. Even with the global style token (GST). Therefore, we propose the following method to successfully train an end-to-end TTS model with limited broadcast data. First, the background music is removed from the speech by introducing a music filter. Second, the GST-TTS model with an auxiliary quality classifier is trained with the filtered speech and a small amount of clean speech. In particular, the quality classifier makes the embedding vector of the GST layer focus on representing the speech quality (filtered or clean) of the input speech. The experimental results verified that the proposed method synthesized much more high-quality speech than conventional methods. |
