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===== Cai, X., Dai, D., Wu, Z., Li, X., Li, J., & Meng, H. (2021). Emotion Controllable Speech Synthesis Using Emotion-Unlabeled Dataset with the Assistance of Cross-Domain Speech Emotion Recognition. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 5734β5738. <nowiki>https://doi.org/10.1109/ICASSP39728.2021.9413907</nowiki> ===== * Summary: This article proposes an approach for emotional TTS synthesis on a dataset without emotion labels, using a cross-domain speech emotion recognition model and an emotional TTS model, hoping to express similar results in specific emotional expressiveness and speech quality compared to models with emotion labels. * RQ: Can we use the achievements and features of SER to solce the problem of lack of emotion-annotated dataset for emotional TTS? * Hypothesis: By performing mean opinion score evaluations and emotion recognition perception evaluation in 4 emotion categories and 2 polarities of emotion dimensions, our GST-based model can generate speech with expected emotions, while trained on a fully emotion-unlabeled dataset. * Conclusion: Through comparing their created cross-domain model with a baseline model, they found that both their 4-categorical model and 2-dimensional model almost achieve as good speech quality as the baseline system, with higher p-values than the significance level of 0.05, indicating no significant differences. Furthermore, they found that both categorical models, one trained on the utterances with highest posterior (top-K scheme) and one trained on the full set of audio, revealed an overall higher accuracy than the baseline model, at 78.75% and 49.25%, respectfully, compared to the baseline 36.75%, which indicated their cross-domain model and top-K scheme were effective in emotional expressiveness. * Critical observations: The choice to use a top-K scheme, as described earlier, is interesting to offset the number of mispredictions made by the SER model, as the SER model is far less reliable that humans. By choosing to use the more reliable audio set, there could be an argument that their choice could inflate their results. However, taking this into account, they did in fact train the model on the full, unaltered, set of audio and still returned a higher accuracy than the baseline model. The fact that this shows promise in returning accurte and sufficient quality in emotional speech synthesis with unlabeled emotion datasets brings promise to a possible increase in speed and efficiency for training other models. * Relevance: The proposed approach, in the authors' words, greatly reduces the threshold of emotional synthesis in regard to amotion-annotated data, reducing the time, cost, and relevant quality of the speech data needed for emotional TTS systems.
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