Emotional Speech Cloning using GANs
Bibliographic record
Abstract
Speech cloning is one of the most sought-after applications of deep learning. While there have been great strides in the field, many have been data-inefficient and have not yielded good results. Furthermore, most synthesized speech is monotonous. The challenge in trying to recreate a given individual's emotions with very limited data is another challenge by itself. In this paper, an alternate approach to speech cloning is proposed by exploring the possibility of treating synthesized voice and synthesized emotion as two separate entities and combining the outputs sequentially. The first part of the proposed network contains a neural voice synthesizer to generate non-emotional speech using as little data as possible. The output of this network is then combined with an array of different speaker emotions and passed to a modified version of CycleGAN network called EmoGAN with the aim being to seamlessly add in different emotions as required in the context of different sentences. The EmoGAN has been trained on two emotions from the Toronto Emotional Speech Set (TESS) database: sadness and anger. This is evident by the convergence plots of generator and discriminator and listening to the synthesized speech and evaluating the subjective quality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".