mlsanityData to 13 Oct 2026

QI-TTS: Questioning Intonation Control for Emotional Speech Synthesis

arXiv 2303.07682 ↗First posted 14 Mar 2023cs.SD cs.CL eess.AScited by 8

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Abstract (arXiv)

Recent expressive text to speech (TTS) models focus on synthesizing emotional speech, but some fine-grained styles such as intonation are neglected. In this paper, we propose QI-TTS which aims to better transfer and control intonation to further deliver the speaker's questioning intention while transferring emotion from reference speech. We propose a multi-style extractor to extract style embedding from two different levels. While the sentence level represents emotion, the final syllable level represents intonation. For fine-grained intonation control, we use relative attributes to represent intonation intensity at the syllable level.Experiments have validated the effectiveness of QI-TTS for improving intonation expressiveness in emotional speech synthesis.

Key facts. Cited by 8 papers in the map (data through 13 Oct 2026). Builds on 14 of its 23 references that are in the map. First posted on arXiv 14 Mar 2023.

Builds on

14 of 23 references.

References · 14

  1. 2015Domain-Adversarial Training of Neural Networkscited by 3,030
  2. 2020HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesiscited by 1,148
  3. 2020FastSpeech 2: Fast and High-Quality End-to-End Text to Speechcited by 715
  4. 2018Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesiscited by 259
  5. 2019Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoderscited by 196
  6. 2018Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotroncited by 172
  7. 2020Seen and Unseen emotional style transfer for voice conversion with a new emotional speech datasetcited by 120
  8. 2021STYLER: Style Factor Modeling with Rapidity and Robustness via Speech Decomposition for Expressive and Controllable Neural Text to Speechcited by 18
  9. 2021EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Modelcited by 18
  10. 2019Towards Unsupervised Speech Recognition and Synthesis with Quantized Speech Representation Learningcited by 16
  11. 2021Adversarially learning disentangled speech representations for robust multi-factor voice conversioncited by 14
  12. 2019Multi-reference Tacotron by Intercross Training for Style Disentangling,Transfer and Control in Speech Synthesiscited by 11
2 more
  1. 2022Into-TTS : Intonation Template Based Prosody Control Systemcited by 2
  2. 2022A Study of Modeling Rising Intonation in Cantonese Neural Speech Synthesiscited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 3; 2025: 3.

8 papers.

  1. 2025EmoVoice: LLM-based Emotional Text-To-Speech Model with Freestyle Text Promptingcited by 34
  2. 2024Laugh Now Cry Later: Controlling Time-Varying Emotional States of Flow-Matching-Based Zero-Shot Text-to-Speechcited by 19
  3. 2024ED-TTS: Multi-Scale Emotion Modeling using Cross-Domain Emotion Diarization for Emotional Speech Synthesiscited by 9
  4. 2025Spotlight-TTS: Spotlighting the Style via Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speechcited by 1
  5. 2023Voice Conversion with Denoising Diffusion Probabilistic GAN Modelscited by 0
  6. 2023AOSR-Net: All-in-One Sandstorm Removal Networkcited by 0
  7. 2024Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learningcited by 0
  8. 2025Voiced-Aware Style Extraction and Style Direction Adjustment for Expressive Text-to-Speechcited by 0