PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS

• URL: http://arxiv.org/abs/2302.12391v3
• Date: Tue, 6 Jun 2023 05:34:19 GMT
• Title: PITS: Variational Pitch Inference without Fundamental Frequency for End-to-End Pitch-controllable TTS
• Authors: Junhyeok Lee, Wonbin Jung, Hyunjae Cho, Jaeyeon Kim, Jaehwan Kim
• Abstract summary: PITS is an end-to-end pitch-controllable text-to-speech model. Pits incorporates the Yingram encoder, the Yingram decoder, and adversarial training of pitch-shifted synthesis to achieve pitch-controllability.
• Score: 1.5599422325061418
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Previous pitch-controllable text-to-speech (TTS) models rely on directly modeling fundamental frequency, leading to low variance in synthesized speech. To address this issue, we propose PITS, an end-to-end pitch-controllable TTS model that utilizes variational inference to model pitch. Based on VITS, PITS incorporates the Yingram encoder, the Yingram decoder, and adversarial training of pitch-shifted synthesis to achieve pitch-controllability. Experiments demonstrate that PITS generates high-quality speech that is indistinguishable from ground truth speech and has high pitch-controllability without quality degradation. Code, audio samples, and demo are available at https://github.com/anonymous-pits/pits.

Related papers

• Flamed-TTS: Flow Matching Attention-Free Models for Efficient Generating and Dynamic Pacing Zero-shot Text-to-Speech [2.5964779217812057]
  Flamed-TTS is a novel zero-shot Text-to-Speech framework that emphasizes low computational cost, low latency, and high speech fidelity alongside rich temporal diversity.<n>We show that Flamed-TTS surpasses state-of-the-art models in terms of intelligibility, naturalness, speaker similarity, acoustic characteristics preservation, and dynamic pace.
  arXiv  Detail & Related papers  (2025-10-03T09:36:55Z)
• Zero-Shot Streaming Text to Speech Synthesis with Transducer and Auto-Regressive Modeling [76.23539797803681]
  Existing methods primarily use a look mechanism, relying on future text to achieve natural streaming speech synthesis.<n>We propose LE, a streaming framework for generating high-quality speech frame-by-frame.<n> Experimental results suggest that the LE outperforms current streaming TTS methods and achieves comparable performance over sentence-level TTS systems.
  arXiv  Detail & Related papers  (2025-05-26T08:25:01Z)
• DMOSpeech: Direct Metric Optimization via Distilled Diffusion Model in Zero-Shot Speech Synthesis [12.310318928818546]
  We introduce DMOSpeech, a distilled diffusion-based TTS model that achieves both faster inference and superior performance compared to its teacher model. Our comprehensive experiments, validated through extensive human evaluation, show significant improvements in naturalness, intelligibility, and speaker similarity while reducing inference time by orders of magnitude. This work establishes a new framework for aligning speech synthesis with human auditory preferences through direct metric optimization.
  arXiv  Detail & Related papers  (2024-10-14T21:17:58Z)
• Autoregressive Speech Synthesis without Vector Quantization [135.4776759536272]
  We present MELLE, a novel continuous-valued tokens based language modeling approach for text to speech synthesis (TTS) MELLE autoregressively generates continuous mel-spectrogram frames directly from text condition.
  arXiv  Detail & Related papers  (2024-07-11T14:36:53Z)
• Schrodinger Bridges Beat Diffusion Models on Text-to-Speech Synthesis [35.16243386407448]
  Bridge-TTS is a novel TTS system that substitutes the noisy Gaussian prior in established diffusion-based TTS methods with a clean and deterministic one. Specifically, we leverage the latent representation obtained from text input as our prior, and build a fully tractable Schrodinger bridge between it and the ground-truth mel-spectrogram.
  arXiv  Detail & Related papers  (2023-12-06T13:31:55Z)
• Pruning Self-Attention for Zero-Shot Multi-Speaker Text-to-Speech [26.533600745910437]
  We propose an effective pruning method for a transformer known as sparse attention, to improve the TTS model's generalization abilities. We also propose a new differentiable pruning method that allows the model to automatically learn the thresholds.
  arXiv  Detail & Related papers  (2023-08-28T21:25:05Z)
• Mega-TTS: Zero-Shot Text-to-Speech at Scale with Intrinsic Inductive Bias [71.94109664001952]
  Mega-TTS is a novel zero-shot TTS system that is trained with large-scale wild data. We show that Mega-TTS surpasses state-of-the-art TTS systems on zero-shot TTS speech editing, and cross-lingual TTS tasks.
  arXiv  Detail & Related papers  (2023-06-06T08:54:49Z)
• Any-speaker Adaptive Text-To-Speech Synthesis with Diffusion Models [65.28001444321465]
  Grad-StyleSpeech is an any-speaker adaptive TTS framework based on a diffusion model. It can generate highly natural speech with extremely high similarity to target speakers' voice, given a few seconds of reference speech. It significantly outperforms speaker-adaptive TTS baselines on English benchmarks.
  arXiv  Detail & Related papers  (2022-11-17T07:17:24Z)
• Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis [19.422230767803246]
  We propose Period VITS, a novel end-to-end text-to-speech model that incorporates an explicit periodicity generator. In the proposed method, we introduce a frame pitch predictor that predicts prosodic features, such as pitch and voicing flags, from the input text. From these features, the proposed periodicity generator produces a sample-level sinusoidal source that enables the waveform decoder to accurately reproduce the pitch.
  arXiv  Detail & Related papers  (2022-10-28T07:52:30Z)
• Unsupervised TTS Acoustic Modeling for TTS with Conditional Disentangled Sequential VAE [36.50265124324876]
  We propose a novel unsupervised text-to-speech acoustic model training scheme, named UTTS, which does not require text-audio pairs. The framework offers a flexible choice of a speaker's duration model, timbre feature (identity) and content for TTS inference. Experiments demonstrate that UTTS can synthesize speech of high naturalness and intelligibility measured by human and objective evaluations.
  arXiv  Detail & Related papers  (2022-06-06T11:51:22Z)
• ProsoSpeech: Enhancing Prosody With Quantized Vector Pre-training in Text-to-Speech [96.0009517132463]
  We introduce a word-level prosody encoder, which quantizes the low-frequency band of the speech and compresses prosody attributes in the latent prosody vector (LPV) We then introduce an LPV predictor, which predicts LPV given word sequence and fine-tune it on the high-quality TTS dataset. Experimental results show that ProsoSpeech can generate speech with richer prosody compared with baseline methods.
  arXiv  Detail & Related papers  (2022-02-16T01:42:32Z)
• On the Interplay Between Sparsity, Naturalness, Intelligibility, and Prosody in Speech Synthesis [102.80458458550999]
  We investigate the tradeoffs between sparstiy and its subsequent effects on synthetic speech. Our findings suggest that not only are end-to-end TTS models highly prunable, but also, perhaps surprisingly, pruned TTS models can produce synthetic speech with equal or higher naturalness and intelligibility.
  arXiv  Detail & Related papers  (2021-10-04T02:03:28Z)
• VaPar Synth -- A Variational Parametric Model for Audio Synthesis [78.3405844354125]
  We present VaPar Synth - a Variational Parametric Synthesizer which utilizes a conditional variational autoencoder (CVAE) trained on a suitable parametric representation. We demonstrate our proposed model's capabilities via the reconstruction and generation of instrumental tones with flexible control over their pitch.
  arXiv  Detail & Related papers  (2020-03-30T16:05:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.