29 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
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
Molecular characterization of tetracycline- and quinolone-resistant Aeromonas salmonicida isolated in Korea
The antibiotic resistance of 16 Aeromonas (A.) salmonicida strains isolated from diseased fish and environmental samples in Korea from 2006 to 2009 were investigated in this study. Tetracycline or quinolone resistance was observed in eight and 16 of the isolates, respectively, based on the measured minimal inhibitory concentrations. Among the tetracycline-resistant strains, seven of the isolates harbored tetA gene and one isolate harbored tetE gene. Additionally, quinolone-resistance determining regions (QRDRs) consisting of the gyrA and parC genes were amplified and sequenced. Among the quinolone-resistant A. salmonicida strains, 15 harbored point mutations in the gyrA codon 83 which were responsible for the corresponding amino acid substitutions of Ser83→Arg83 or Ser83→Asn83. We detected no point mutations in other QRDRs, such as gyrA codons 87 and 92, and parC codons 80 and 84. Genetic similarity was assessed via pulsed-field gel electrophoresis, and the results indicated high clonality among the Korean antibiotic-resistant strains of A. salmonicida
Large-scale learning of generalised representations for speaker recognition
The objective of this work is to develop a speaker recognition model to be
used in diverse scenarios. We hypothesise that two components should be
adequately configured to build such a model. First, adequate architecture would
be required. We explore several recent state-of-the-art models, including
ECAPA-TDNN and MFA-Conformer, as well as other baselines. Second, a massive
amount of data would be required. We investigate several new training data
configurations combining a few existing datasets. The most extensive
configuration includes over 87k speakers' 10.22k hours of speech. Four
evaluation protocols are adopted to measure how the trained model performs in
diverse scenarios. Through experiments, we find that MFA-Conformer with the
least inductive bias generalises the best. We also show that training with
proposed large data configurations gives better performance. A boost in
generalisation is observed, where the average performance on four evaluation
protocols improves by more than 20%. In addition, we also demonstrate that
these models' performances can improve even further when increasing capacity.Comment: 5pages, 5 tables, submitted to ICASS
Baseline Systems for the First Spoofing-Aware Speaker Verification Challenge: Score and Embedding Fusion
Deep learning has brought impressive progress in the study of both automatic
speaker verification (ASV) and spoofing countermeasures (CM). Although
solutions are mutually dependent, they have typically evolved as standalone
sub-systems whereby CM solutions are usually designed for a fixed ASV system.
The work reported in this paper aims to gauge the improvements in reliability
that can be gained from their closer integration. Results derived using the
popular ASVspoof2019 dataset indicate that the equal error rate (EER) of a
state-of-the-art ASV system degrades from 1.63% to 23.83% when the evaluation
protocol is extended with spoofed trials.%subjected to spoofing attacks.
However, even the straightforward integration of ASV and CM systems in the form
of score-sum and deep neural network-based fusion strategies reduce the EER to
1.71% and 6.37%, respectively. The new Spoofing-Aware Speaker Verification
(SASV) challenge has been formed to encourage greater attention to the
integration of ASV and CM systems as well as to provide a means to benchmark
different solutions.Comment: 8 pages, accepted by Odyssey 202
Identifying novel genetic variants for brain amyloid deposition: a genome-wide association study in the Korean population
Background: Genome-wide association studies (GWAS) have identified a number of genetic variants for Alzheimer's disease (AD). However, most GWAS were conducted in individuals of European ancestry, and non-European populations are still underrepresented in genetic discovery efforts. Here, we performed GWAS to identify single nucleotide polymorphisms (SNPs) associated with amyloid β (Aβ) positivity using a large sample of Korean population.
Methods: One thousand four hundred seventy-four participants of Korean ancestry were recruited from multicenters in South Korea. Discovery dataset consisted of 1190 participants (383 with cognitively unimpaired [CU], 330 with amnestic mild cognitive impairment [aMCI], and 477 with AD dementia [ADD]) and replication dataset consisted of 284 participants (46 with CU, 167 with aMCI, and 71 with ADD). GWAS was conducted to identify SNPs associated with Aβ positivity (measured by amyloid positron emission tomography). Aβ prediction models were developed using the identified SNPs. Furthermore, bioinformatics analysis was conducted for the identified SNPs.
Results: In addition to APOE, we identified nine SNPs on chromosome 7, which were associated with a decreased risk of Aβ positivity at a genome-wide suggestive level. Of these nine SNPs, four novel SNPs (rs73375428, rs2903923, rs3828947, and rs11983537) were associated with a decreased risk of Aβ positivity (p < 0.05) in the replication dataset. In a meta-analysis, two SNPs (rs7337542 and rs2903923) reached a genome-wide significant level (p < 5.0 × 10-8). Prediction performance for Aβ positivity increased when rs73375428 were incorporated (area under curve = 0.75; 95% CI = 0.74-0.76) in addition to clinical factors and APOE genotype. Cis-eQTL analysis demonstrated that the rs73375428 was associated with decreased expression levels of FGL2 in the brain.
Conclusion: The novel genetic variants associated with FGL2 decreased risk of Aβ positivity in the Korean population. This finding may provide a candidate therapeutic target for AD, highlighting the importance of genetic studies in diverse populations
Comparison of the Antibacterial Properties of Phage Endolysins SAL-1 and LysK▿
In spite of the high degree of amino acid sequence similarity between the newly discovered phage endolysin SAL-1 and the phage endolysin LysK, SAL-1 has an approximately 2-fold-lower MIC against several Staphylococcus aureus strains and higher bacterial cell-wall-hydrolyzing activity than LysK. The amino acid residue change contributing the most to this enhanced enzymatic activity is a change from glutamic acid to glutamine at the 114th residue