| Paper ID | SPE-4.3 |
| Paper Title |
PROSODIC CLUSTERING FOR PHONEME-LEVEL PROSODY CONTROL IN END-TO-END SPEECH SYNTHESIS |
| Authors |
Alexandra Vioni, Myrsini Christidou, Nikolaos Ellinas, Georgios Vamvoukakis, Panos Kakoulidis, Innoetics, Samsung Electronics, Greece; Taehoon Kim, June Sig Sung, Hyoungmin Park, Mobile Communications Business, Samsung Electronics, South Korea; Aimilios Chalamandaris, Pirros Tsiakoulis, Innoetics, Samsung Electronics, Greece |
| Session | SPE-4: Speech Synthesis 2: Controllability |
| Location | Gather.Town |
| Session Time: | Tuesday, 08 June, 13:00 - 13:45 |
| Presentation Time: | Tuesday, 08 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 |
This paper presents a method for controlling the prosody at the phoneme level in an autoregressive attention-based text-to-speech system. Instead of learning latent prosodic features with a variational framework as is commonly done, we directly extract phoneme-level F0 and duration features from the speech data in the training set. Each prosodic feature is discretized using unsupervised clustering in order to produce a sequence of prosodic labels for each utterance. This sequence is used in parallel to the phoneme sequence in order to condition the decoder with the utilization of a prosodic encoder and a corresponding attention module. Experimental results show that the proposed method retains the high quality of generated speech, while allowing phoneme-level control of F0 and duration. By replacing the F0 cluster centroids with musical notes, the model can also provide control over the note and octave within the range of the speaker. |
