Controllable Multi-Speaker Emotional Speech Synthesis With an Emotion Representation of High Generalization Capability
Bibliographic record
Abstract
The aim of multi-speaker emotional speech synthesis is to generate speech for a designated speaker in a desired emotional state. The task is challenging due to the presence of speech variations, such as noise, content, and timbre, which can obstruct emotion extraction and transfer. This paper proposes a new approach to performing multi-speaker emotional speech synthesis. The proposed method, which is based on a seq2seq synthesizer, integrates emotion embedding as a conditioned variable to convey exact emotional information from reference audio to the synthesized speech. To boost emotion representation capability, we utilize a three-dimensional acoustic feature as input. And an emotion generalization module with adaptive instance normalization (AdaIN) is proposed to obtain emotion embedding with high generalization ability, which also results in improved controllability. The derived emotion embedding from the generalization module can be readily conditioned by affine parameters, allowing for control both the emotion category and the emotion intensity of synthesized speech. Various emotional speech synthesis experimental results of the propposed method demonstrate its state-of-the-art performance in multi-speaker emotional speech synthesis, coupled with its advantage of high emotion controllability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".