Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations

• URL: http://arxiv.org/abs/2206.12662v1
• Date: Sat, 25 Jun 2022 14:27:10 GMT
• Title: Synthesizing Personalized Non-speech Vocalization from Discrete Speech Representations
• Authors: Chin-Cheng Hsu
• Abstract summary: We formulated non-speech vocalization (NSV) modeling as a text-to-speech task and verified its viability. Specifically, we evaluated the phonetic expressivity of HUBERT speech units on NSVs and verified our model's ability to control over speaker timbre.
• Score: 3.0016140723286457
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We formulated non-speech vocalization (NSV) modeling as a text-to-speech task and verified its viability. Specifically, we evaluated the phonetic expressivity of HUBERT speech units on NSVs and verified our model's ability to control over speaker timbre even though the training data is speaker few-shot. In addition, we substantiated that the heterogeneity in recording conditions is the major obstacle for NSV modeling. Finally, we discussed five improvements over our method for future research. Audio samples of synthesized NSVs are available on our demo page: https://resemble-ai.github.io/reLaugh.

Related papers

• TransVIP: Speech to Speech Translation System with Voice and Isochrony Preservation [97.54885207518946]
  We introduce a novel model framework TransVIP that leverages diverse datasets in a cascade fashion. We propose two separated encoders to preserve the speaker's voice characteristics and isochrony from the source speech during the translation process. Our experiments on the French-English language pair demonstrate that our model outperforms the current state-of-the-art speech-to-speech translation model.
  arXiv  Detail & Related papers  (2024-05-28T04:11:37Z)
• Noise-robust zero-shot text-to-speech synthesis conditioned on self-supervised speech-representation model with adapters [47.75276947690528]
  The zero-shot text-to-speech (TTS) method can reproduce speaker characteristics very accurately. However, this approach suffers from degradation in speech synthesis quality when the reference speech contains noise. In this paper, we propose a noise-robust zero-shot TTS method.
  arXiv  Detail & Related papers  (2024-01-10T12:21:21Z)
• SelfVC: Voice Conversion With Iterative Refinement using Self Transformations [42.97689861071184]
  SelfVC is a training strategy to improve a voice conversion model with self-synthesized examples. We develop techniques to derive prosodic information from the audio signal and SSL representations to train predictive submodules in the synthesis model. Our framework is trained without any text and achieves state-of-the-art results in zero-shot voice conversion on metrics evaluating naturalness, speaker similarity, and intelligibility of synthesized audio.
  arXiv  Detail & Related papers  (2023-10-14T19:51:17Z)
• SpeechX: Neural Codec Language Model as a Versatile Speech Transformer [57.82364057872905]
  SpeechX is a versatile speech generation model capable of zero-shot TTS and various speech transformation tasks. Experimental results show SpeechX's efficacy in various tasks, including zero-shot TTS, noise suppression, target speaker extraction, speech removal, and speech editing with or without background noise.
  arXiv  Detail & Related papers  (2023-08-14T01:01:19Z)
• Visual-Aware Text-to-Speech [101.89332968344102]
  We present a new visual-aware text-to-speech (VA-TTS) task to synthesize speech conditioned on both textual inputs and visual feedback of the listener in face-to-face communication. We devise a baseline model to fuse phoneme linguistic information and listener visual signals for speech synthesis.
  arXiv  Detail & Related papers  (2023-06-21T05:11:39Z)
• Zero-shot text-to-speech synthesis conditioned using self-supervised speech representation model [13.572330725278066]
  A novel point of the proposed method is the direct use of the SSL model to obtain embedding vectors from speech representations trained with a large amount of data. The disentangled embeddings will enable us to achieve better reproduction performance for unseen speakers and rhythm transfer conditioned by different speeches.
  arXiv  Detail & Related papers  (2023-04-24T10:15:58Z)
• Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations [51.89856133895233]
  Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a new SR application. To make our SR model robust against various degradation, we use (i) a speech representation extracted from w2v-BERT for the input feature, and (ii) a text representation extracted from transcripts via PnG-BERT as a linguistic conditioning feature.
  arXiv  Detail & Related papers  (2023-03-03T01:57:16Z)
• Time out of Mind: Generating Rate of Speech conditioned on emotion and speaker [0.0]
  We train a GAN conditioned on emotion to generate worth lengths for a given input text. These word lengths are relative neutral speech and can be provided to a text-to-speech system to generate more expressive speech. We were able to achieve better performances on objective measures for neutral speech, and better time alignment for happy speech when compared to an out-of-box model.
  arXiv  Detail & Related papers  (2023-01-29T02:58:01Z)
• 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)
• 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)
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis [67.73554826428762]
  We propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech, enabling efficient synthesis of clean, realistic speech from noisy signals.
  arXiv  Detail & Related papers  (2022-03-31T17:57:10Z)

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.