Controllable Multi-Speaker Emotional Speech Synthesis With an Emotion Representation of High Generalization Capability
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it