Prosodic Clustering for Phoneme-level Prosody Control in End-to-End Speech Synthesis
This work addresses the problem of fine-grained prosody manipulation for speech synthesis users, offering an incremental improvement over existing variational methods.
The paper tackles phoneme-level prosody control in end-to-end speech synthesis by directly extracting and discretizing F0 and duration features via unsupervised clustering, enabling high-quality speech generation with explicit control over these prosodic elements, including musical note substitution.
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.