An analysis on the effects of speaker embedding choice in non auto-regressive TTS

• URL: http://arxiv.org/abs/2307.09898v1
• Date: Wed, 19 Jul 2023 10:57:54 GMT
• Title: An analysis on the effects of speaker embedding choice in non auto-regressive TTS
• Authors: Adriana Stan and Johannah O'Mahony
• Abstract summary: We introduce a first attempt on understanding how a non-autoregressive factorised multi-speaker speech synthesis architecture exploits the information present in different speaker embedding sets. We show that, regardless of the used set of embeddings and learning strategy, the network can handle various speaker identities equally well.
• Score: 4.619541348328938
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper we introduce a first attempt on understanding how a non-autoregressive factorised multi-speaker speech synthesis architecture exploits the information present in different speaker embedding sets. We analyse if jointly learning the representations, and initialising them from pretrained models determine any quality improvements for target speaker identities. In a separate analysis, we investigate how the different sets of embeddings impact the network's core speech abstraction (i.e. zero conditioned) in terms of speaker identity and representation learning. We show that, regardless of the used set of embeddings and learning strategy, the network can handle various speaker identities equally well, with barely noticeable variations in speech output quality, and that speaker leakage within the core structure of the synthesis system is inevitable in the standard training procedures adopted thus far.

Related papers

• Integrating Audio, Visual, and Semantic Information for Enhanced Multimodal Speaker Diarization [25.213694510527436]
  Most existing speaker diarization systems rely exclusively on unimodal acoustic information. We propose a novel multimodal approach that jointly utilizes audio, visual, and semantic cues to enhance speaker diarization. Our approach consistently outperforms state-of-the-art speaker diarization methods.
  arXiv  Detail & Related papers  (2024-08-22T03:34:03Z)
• Learning Disentangled Speech Representations [0.412484724941528]
  SynSpeech is a novel large-scale synthetic speech dataset designed to enable research on disentangled speech representations. We present a framework to evaluate disentangled representation learning techniques, applying both linear probing and established supervised disentanglement metrics. We find that SynSpeech facilitates benchmarking across a range of factors, achieving promising disentanglement of simpler features like gender and speaking style, while highlighting challenges in isolating complex attributes like speaker identity.
  arXiv  Detail & Related papers  (2023-11-04T04:54:17Z)
• Learning Separable Hidden Unit Contributions for Speaker-Adaptive Lip-Reading [73.59525356467574]
  A speaker's own characteristics can always be portrayed well by his/her few facial images or even a single image with shallow networks. Fine-grained dynamic features associated with speech content expressed by the talking face always need deep sequential networks. Our approach consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-08T07:48:25Z)
• Improving Speaker Diarization using Semantic Information: Joint Pairwise Constraints Propagation [53.01238689626378]
  We propose a novel approach to leverage semantic information in speaker diarization systems. We introduce spoken language understanding modules to extract speaker-related semantic information. We present a novel framework to integrate these constraints into the speaker diarization pipeline.
  arXiv  Detail & Related papers  (2023-09-19T09:13:30Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• Self-supervised Fine-tuning for Improved Content Representations by Speaker-invariant Clustering [78.2927924732142]
  We propose speaker-invariant clustering (Spin) as a novel self-supervised learning method. Spin disentangles speaker information and preserves content representations with just 45 minutes of fine-tuning on a single GPU.
  arXiv  Detail & Related papers  (2023-05-18T15:59:36Z)
• Residual Information in Deep Speaker Embedding Architectures [4.619541348328938]
  This paper introduces an analysis over six sets of speaker embeddings extracted with some of the most recent and high-performing DNN architectures. The dataset includes 46 speakers uttering the same set of prompts, recorded in either a professional studio or their home environments. The results show that the discriminative power of the analyzed embeddings is very high, yet across all the analyzed architectures, residual information is still present in the representations.
  arXiv  Detail & Related papers  (2023-02-06T12:37:57Z)
• Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning [58.14807331265752]
  We show that better speaker embeddings can be learned by momentum contrastive learning. We generalize the self-supervised framework to a semi-supervised scenario where only a small portion of the data is labeled.
  arXiv  Detail & Related papers  (2020-12-13T23:23:39Z)
• Disentangled Speech Embeddings using Cross-modal Self-supervision [119.94362407747437]
  We develop a self-supervised learning objective that exploits the natural cross-modal synchrony between faces and audio in video. We construct a two-stream architecture which: (1) shares low-level features common to both representations; and (2) provides a natural mechanism for explicitly disentangling these factors.
  arXiv  Detail & Related papers  (2020-02-20T14:13:12Z)

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.