Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation

• URL: http://arxiv.org/abs/2210.12635v2
• Date: Wed, 26 Oct 2022 04:48:44 GMT
• Title: Quantitative Evidence on Overlooked Aspects of Enrollment Speaker Embeddings for Target Speaker Separation
• Authors: Xiaoyu Liu, Xu Li, Joan Serr\`a
• Abstract summary: Single channel target speaker separation aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an upstream model that obtains enrollment speaker embeddings and a downstream model that performs the separation conditioned on the embeddings.
• Score: 14.013049471563141
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single channel target speaker separation (TSS) aims at extracting a speaker's voice from a mixture of multiple talkers given an enrollment utterance of that speaker. A typical deep learning TSS framework consists of an upstream model that obtains enrollment speaker embeddings and a downstream model that performs the separation conditioned on the embeddings. In this paper, we look into several important but overlooked aspects of the enrollment embeddings, including the suitability of the widely used speaker identification embeddings, the introduction of the log-mel filterbank and self-supervised embeddings, and the embeddings' cross-dataset generalization capability. Our results show that the speaker identification embeddings could lose relevant information due to a sub-optimal metric, training objective, or common pre-processing. In contrast, both the filterbank and the self-supervised embeddings preserve the integrity of the speaker information, but the former consistently outperforms the latter in a cross-dataset evaluation. The competitive separation and generalization performance of the previously overlooked filterbank embedding is consistent across our study, which calls for future research on better upstream features.

Related papers

• 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)
• An analysis on the effects of speaker embedding choice in non auto-regressive TTS [4.619541348328938]
  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.
  arXiv  Detail & Related papers  (2023-07-19T10:57:54Z)
• 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)
• Content-Aware Speaker Embeddings for Speaker Diarisation [3.6398652091809987]
  The content-aware speaker embeddings (CASE) approach is proposed. Case factorises automatic speech recognition (ASR) from speaker recognition to focus on modelling speaker characteristics. Case achieved a 17.8% relative speaker error rate reduction over conventional methods.
  arXiv  Detail & Related papers  (2021-02-12T12:02:03Z)
• 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)
• Speaker Separation Using Speaker Inventories and Estimated Speech [78.57067876891253]
  We propose speaker separation using speaker inventories (SSUSI) and speaker separation using estimated speech (SSUES) By combining the advantages of permutation invariant training (PIT) and speech extraction, SSUSI significantly outperforms conventional approaches.
  arXiv  Detail & Related papers  (2020-10-20T18:15:45Z)
• Active Speakers in Context [88.22935329360618]
  Current methods for active speak er detection focus on modeling short-term audiovisual information from a single speaker. This paper introduces the Active Speaker Context, a novel representation that models relationships between multiple speakers over long time horizons. Our experiments show that a structured feature ensemble already benefits the active speaker detection performance.
  arXiv  Detail & Related papers  (2020-05-20T01:14:23Z)
• Speaker Diarization with Lexical Information [59.983797884955]
  This work presents a novel approach for speaker diarization to leverage lexical information provided by automatic speech recognition. We propose a speaker diarization system that can incorporate word-level speaker turn probabilities with speaker embeddings into a speaker clustering process to improve the overall diarization accuracy.
  arXiv  Detail & Related papers  (2020-04-13T17:16:56Z)
• Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
  This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
  arXiv  Detail & Related papers  (2020-02-14T05:05:36Z)

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.