mlsanityData to 7 Oct 2026

Analysis and Assessment of Controllability of an Expressive Deep Learning-based TTS system

arXiv 2103.04097 ↗First posted 6 Mar 2021cs.SD cs.AI cs.CLcited by 4

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Abstract (arXiv)

In this paper, we study the controllability of an Expressive TTS system trained on a dataset for a continuous control. The dataset is the Blizzard 2013 dataset based on audiobooks read by a female speaker containing a great variability in styles and expressiveness. Controllability is evaluated with both an objective and a subjective experiment. The objective assessment is based on a measure of correlation between acoustic features and the dimensions of the latent space representing expressiveness. The subjective assessment is based on a perceptual experiment in which users are shown an interface for Controllable Expressive TTS and asked to retrieve a synthetic utterance whose expressiveness subjectively corresponds to that a reference utterance.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 6 Mar 2021.

Builds on

15 of 27 references.

References · 15

  1. 2016WaveNet: A Generative Model for Raw Audiocited by 1,710
  2. 2017Tacotron: Towards End-to-End Speech Synthesiscited by 487
  3. 2018Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesiscited by 257
  4. 2018Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotroncited by 171
  5. 2018Hierarchical Generative Modeling for Controllable Speech Synthesiscited by 93
  6. 2017Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attentioncited by 57
  7. 2019Fine-grained robust prosody transfer for single-speaker neural text-to-speechcited by 27
  8. 2018Expressive Speech Synthesis via Modeling Expressions with Variational Autoencodercited by 22
  9. 2020CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speechcited by 22
  10. 2020Controllable neural text-to-speech synthesis using intuitive prosodic featurescited by 19
  11. 2018Deep Encoder-Decoder Models for Unsupervised Learning of Controllable Speech Synthesiscited by 13
  12. 2019Sequence to Sequence Neural Speech Synthesis with Prosody Modification Capabilitiescited by 11
3 more
  1. 2019Visualization and Interpretation of Latent Spaces for Controlling Expressive Speech Synthesis through Audio Analysiscited by 8
  2. 2019A Methodology for Controlling the Emotional Expressiveness in Synthetic Speech -- a Deep Learning approachcited by 3
  3. 2019The Theory behind Controllable Expressive Speech Synthesis: a Cross-disciplinary Approachcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2021A Survey on Neural Speech Synthesiscited by 182
  2. 2023MUST&P-SRL: Multi-lingual and Unified Syllabification in Text and Phonetic Domains for Speech Representation Learningcited by 1
  3. 2024TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfercited by 0
  4. 2026Voice Mapping of Text-to-Speech Systems: A Metric-Based Approach for Voice Quality Assessmentcited by 0