REVIEW 4 cited by
EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
State-of-the-art speech synthesis models try to get as close as possible to the human voice. Hence, modelling emotions is an essential part of Text-To-Speech (TTS) research. In our work, we selected FastSpeech2 as the starting point and proposed a series of modifications for synthesizing emotional speech. According to automatic and human evaluation, our model, EmoSpeech, surpasses existing models regarding both MOS score and emotion recognition accuracy in generated speech. We provided a detailed ablation study for every extension to FastSpeech2 architecture that forms EmoSpeech. The uneven distribution of emotions in the text is crucial for better, synthesized speech and intonation perception. Our model includes a conditioning mechanism that effectively handles this issue by allowing emotions to contribute to each phone with varying intensity levels. The human assessment indicates that proposed modifications generate audio with higher MOS and emotional expressiveness.
Forward citations
Cited by 4 Pith papers
-
Beyond One-Size-Fits-All: Personalized and Culturally Adaptive Emotional TTS via Interactive Optimization of Individual Emotion Perception Spaces
An interactive genetic algorithm tunes arousal-valence coordinates per listener in emotional TTS, and personalized or culture-specific coordinates beat a generic U.S.-average baseline in small A/B tests.
-
MultiActor-Audiobook: Zero-Shot Audiobook Generation with Faces and Voices of Multiple Speakers
A zero-shot pipeline that creates character voices from AI-generated faces and LLM-written prosody instructions can produce expressive audiobooks without extra training or manual annotation, though human quality score...
-
Marco-Voice Technical Report
Marco-Voice is a TTS system combining voice cloning and emotional speech generation via speaker-emotion disentanglement, contrastive learning, and a new Mandarin emotional dataset, with claimed quality gains over Cosy...
-
A Review of Human Emotion Synthesis Based on Generative Technology
A systematic review that taxonomizes roughly 230 papers on generative-model-based emotion synthesis across faces, speech, and text, and catalogs datasets, metrics, and future directions.
Discussion (0). Continue with ORCID to comment.