JOURNAL ARTICLE

EMOQ-TTS: Emotion Intensity Quantization for Fine-Grained Controllable Emotional Text-to-Speech

Chae-Bin ImSang-Hoon LeeSeung-Bin KimSeong‐Whan Lee

Year: 2022 Journal:   ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pages: 6317-6321

Abstract

Although recent advances in text-to-speech (TTS) have shown significant improvement, it is still limited to emotional speech synthesis. To produce emotional speech, most works utilize emotion information extracted from emotion labels or reference audio. However, they result in monotonous emotional expression due to the utterance-level emotion conditions. In this paper, we propose EmoQ-TTS, which synthesizes expressive emotional speech by conditioning phoneme-wise emotion information with fine-grained emotion intensity. Here, the intensity of emotion information is rendered by distance-based intensity quantization without human labeling. We can also control the emotional expression of synthesized speech by conditioning intensity labels manually. The experimental results demonstrate the superiority of EmoQ-TTS in emotional expressiveness and controllability.

Keywords:
Utterance Computer science Speech recognition Controllability Quantization (signal processing) Speech synthesis Emotional expression Artificial intelligence Psychology Cognitive psychology Mathematics Computer vision

Metrics

39
Cited By
4.58
FWCI (Field Weighted Citation Impact)
42
Refs
0.95
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Speech Recognition and Synthesis
Physical Sciences →  Computer Science →  Artificial Intelligence
Speech and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
Music and Audio Processing
Physical Sciences →  Computer Science →  Signal Processing
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