# Speaker Adaption with Intuitive Prosodic Features for Statistical Parametric Speech Synthesis

arXiv: https://arxiv.org/abs/2203.00951 · HTML: https://mlsanity.com/paper/2203.00951/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 27 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 17 of 17)

- [Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions](https://mlsanity.com/paper/1712.05884/) (2017)
- [FastSpeech 2: Fast and High-Quality End-to-End Text to Speech](https://mlsanity.com/paper/2006.04558/) (2020)
- [Parallel WaveGAN: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram](https://mlsanity.com/paper/1910.11480/) (2019)
- [Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis](https://mlsanity.com/paper/1806.04558/) (2018)
- [Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis](https://mlsanity.com/paper/1803.09017/) (2018)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [Neural Voice Cloning with a Few Samples](https://mlsanity.com/paper/1802.06006/) (2018)
- [AdaSpeech: Adaptive Text to Speech for Custom Voice](https://mlsanity.com/paper/2103.00993/) (2021)
- [Zero-Shot Multi-Speaker Text-To-Speech with State-of-the-art Neural Speaker Embeddings](https://mlsanity.com/paper/1910.10838/) (2019)
- [Learning latent representations for style control and transfer in end-to-end speech synthesis](https://mlsanity.com/paper/1812.04342/) (2018)
- [Sample Efficient Adaptive Text-to-Speech](https://mlsanity.com/paper/1809.10460/) (2018)
- [Attentron: Few-Shot Text-to-Speech Utilizing Attention-Based Variable-Length Embedding](https://mlsanity.com/paper/2005.08484/) (2020)
- [Controllable neural text-to-speech synthesis using intuitive prosodic features](https://mlsanity.com/paper/2009.06775/) (2020)
- [Fitting New Speakers Based on a Short Untranscribed Sample](https://mlsanity.com/paper/1802.06984/) (2018)
- [Machine Speech Chain with One-shot Speaker Adaptation](https://mlsanity.com/paper/1803.10525/) (2018)
- [High quality, lightweight and adaptable TTS using LPCNet](https://mlsanity.com/paper/1905.00590/) (2019)
- [Modeling Multi-speaker Latent Space to Improve Neural TTS: Quick Enrolling New Speaker and Enhancing Premium Voice](https://mlsanity.com/paper/1812.05253/) (2018)

## Built on by (most cited first, 1 of 1)

- [Voice Cloning: Comprehensive Survey](https://mlsanity.com/paper/2505.00579/) (2025)

## Abstract (arXiv)

In this paper, we propose a method of speaker adaption with intuitive prosodic features for statistical parametric speech synthesis. The intuitive prosodic features employed in this method include pitch, pitch range, speech rate and energy considering that they are directly related with the overall prosodic characteristics of different speakers. The intuitive prosodic features are extracted at utterance-level or speaker-level, and are further integrated into the existing speaker-encoding-based and speaker-embedding-based adaptation frameworks respectively. The acoustic models are sequence-to-sequence ones based on Tacotron2. Intuitive prosodic features are concatenated with text encoder outputs and speaker vectors for decoding acoustic features.Experimental results have demonstrated that our proposed methods can achieve better objective and subjective performance than the baseline methods without intuitive prosodic features. Besides, the proposed speaker adaption method with utterance-level prosodic features has achieved the best similarity of synthetic speech among all compared methods.