694 research outputs found
Disentangled representation learning for multilingual speaker recognition
The goal of this paper is to learn robust speaker representation for
bilingual speaking scenario. The majority of the world's population speak at
least two languages; however, most speaker recognition systems fail to
recognise the same speaker when speaking in different languages.
Popular speaker recognition evaluation sets do not consider the bilingual
scenario, making it difficult to analyse the effect of bilingual speakers on
speaker recognition performance. In this paper, we publish a large-scale
evaluation set named VoxCeleb1-B derived from VoxCeleb that considers bilingual
scenarios.
We introduce an effective disentanglement learning strategy that combines
adversarial and metric learning-based methods. This approach addresses the
bilingual situation by disentangling language-related information from speaker
representation while ensuring stable speaker representation learning. Our
language-disentangled learning method only uses language pseudo-labels without
manual information.Comment: Interspeech 202
ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics
Summary: ArrayXPath () is a web-based service for mapping and visualizing microarray gene-expression data with integrated biological pathway resources using Scalable Vector Graphics (SVG). Deciphering the crosstalk among pathways and integrating biomedical ontologies and knowledge bases may help biological interpretation of microarray data. ArrayXPath is empowered by integrating gene-pathway, disease-pathway, drug-pathway and pathway–pathway correlations with integrated Gene Ontology, Medical Subject Headings and OMIM Morbid Map-based annotations. We applied Fisher's exact test and relative risk to evaluate the statistical significance of the correlations. ArrayXPath produces Javascript-enabled SVGs for web-enabled interactive visualization of gene-expression profiles integrated with gene-pathway-disease interactions enriched by biomedical ontologies
Disentangled dimensionality reduction for noise-robust speaker diarisation
The objective of this work is to train noise-robust speaker embeddings
adapted for speaker diarisation. Speaker embeddings play a crucial role in the
performance of diarisation systems, but they often capture spurious information
such as noise and reverberation, adversely affecting performance. Our previous
work has proposed an auto-encoder-based dimensionality reduction module to help
remove the redundant information. However, they do not explicitly separate such
information and have also been found to be sensitive to hyper-parameter values.
To this end, we propose two contributions to overcome these issues: (i) a novel
dimensionality reduction framework that can disentangle spurious information
from the speaker embeddings; (ii) the use of a speech/non-speech indicator to
prevent the speaker code from representing the background noise. Through a
range of experiments conducted on four different datasets, our approach
consistently demonstrates the state-of-the-art performance among models without
system fusion.Comment: This paper was submitted to Interspeech202
- …