Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion

• URL: http://arxiv.org/abs/2011.01678v2
• Date: Thu, 17 Jun 2021 12:50:49 GMT
• Title: Learning Explicit Prosody Models and Deep Speaker Embeddings for Atypical Voice Conversion
• Authors: Disong Wang, Songxiang Liu, Lifa Sun, Xixin Wu, Xunying Liu and Helen Meng
• Abstract summary: We propose a VC system with explicit prosodic modelling and deep speaker embedding learning. A prosody corrector takes in phoneme embeddings to infer typical phoneme duration and pitch values. A conversion model takes phoneme embeddings and typical prosody features as inputs to generate the converted speech.
• Score: 60.808838088376675
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Though significant progress has been made for the voice conversion (VC) of typical speech, VC for atypical speech, e.g., dysarthric and second-language (L2) speech, remains a challenge, since it involves correcting for atypical prosody while maintaining speaker identity. To address this issue, we propose a VC system with explicit prosodic modelling and deep speaker embedding (DSE) learning. First, a speech-encoder strives to extract robust phoneme embeddings from atypical speech. Second, a prosody corrector takes in phoneme embeddings to infer typical phoneme duration and pitch values. Third, a conversion model takes phoneme embeddings and typical prosody features as inputs to generate the converted speech, conditioned on the target DSE that is learned via speaker encoder or speaker adaptation. Extensive experiments demonstrate that speaker adaptation can achieve higher speaker similarity, and the speaker encoder based conversion model can greatly reduce dysarthric and non-native pronunciation patterns with improved speech intelligibility. A comparison of speech recognition results between the original dysarthric speech and converted speech show that absolute reduction of 47.6% character error rate (CER) and 29.3% word error rate (WER) can be achieved.

Related papers

• CoLM-DSR: Leveraging Neural Codec Language Modeling for Multi-Modal Dysarthric Speech Reconstruction [61.067153685104394]
  Dysarthric speech reconstruction (DSR) aims to transform dysarthric speech into normal speech. It still suffers from low speaker similarity and poor prosody naturalness. We propose a multi-modal DSR model by leveraging neural language modeling to improve the reconstruction results.
  arXiv  Detail & Related papers  (2024-06-12T15:42:21Z)
• Self-Supervised Speech Representations Preserve Speech Characteristics while Anonymizing Voices [15.136348385992047]
  We train several voice conversion models using self-supervised speech representations. Converted voices retain a low word error rate within 1% of the original voice. Experiments on dysarthric speech data show that speech features relevant to articulation, prosody, phonation and phonology can be extracted from anonymized voices.
  arXiv  Detail & Related papers  (2022-04-04T17:48:01Z)
• Speaker Identity Preservation in Dysarthric Speech Reconstruction by Adversarial Speaker Adaptation [59.41186714127256]
  Dysarthric speech reconstruction (DSR) aims to improve the quality of dysarthric speech. Speaker encoder (SE) optimized for speaker verification has been explored to control the speaker identity. We propose a novel multi-task learning strategy, i.e., adversarial speaker adaptation (ASA)
  arXiv  Detail & Related papers  (2022-02-18T08:59:36Z)
• On Prosody Modeling for ASR+TTS based Voice Conversion [82.65378387724641]
  In voice conversion, an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents. Such a paradigm, referred to as ASR+TTS, overlooks the modeling of prosody, which plays an important role in speech naturalness and conversion similarity. We propose to directly predict prosody from the linguistic representation in a target-speaker-dependent manner, referred to as target text prediction (TTP)
  arXiv  Detail & Related papers  (2021-07-20T13:30:23Z)
• VQMIVC: Vector Quantization and Mutual Information-Based Unsupervised Speech Representation Disentanglement for One-shot Voice Conversion [54.29557210925752]
  One-shot voice conversion can be effectively achieved by speech representation disentanglement. We employ vector quantization (VQ) for content encoding and introduce mutual information (MI) as the correlation metric during training. Experimental results reflect the superiority of the proposed method in learning effective disentangled speech representations.
  arXiv  Detail & Related papers  (2021-06-18T13:50:38Z)
• Towards Natural and Controllable Cross-Lingual Voice Conversion Based on Neural TTS Model and Phonetic Posteriorgram [21.652906261475533]
  Cross-lingual voice conversion is a challenging problem due to significant mismatches of the phonetic set and the speech prosody of different languages. We build upon the neural text-to-speech (TTS) model to design a new cross-lingual VC framework named FastSpeech-VC.
  arXiv  Detail & Related papers  (2021-02-03T10:28:07Z)
• Adversarially learning disentangled speech representations for robust multi-factor voice conversion [39.91395314356084]
  We propose a disentangled speech representation learning framework based on adversarial learning. Four speech representations characterizing content, timbre, rhythm and pitch are extracted, and further disentangled. Experimental results show that the proposed framework significantly improves the robustness of VC on multiple factors.
  arXiv  Detail & Related papers  (2021-01-30T08:29:55Z)
• Bridging the Modality Gap for Speech-to-Text Translation [57.47099674461832]
  End-to-end speech translation aims to translate speech in one language into text in another language via an end-to-end way. Most existing methods employ an encoder-decoder structure with a single encoder to learn acoustic representation and semantic information simultaneously. We propose a Speech-to-Text Adaptation for Speech Translation model which aims to improve the end-to-end model performance by bridging the modality gap between speech and text.
  arXiv  Detail & Related papers  (2020-10-28T12:33:04Z)

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.