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

Rethinking Session Variability: Leveraging Session Embeddings for Session Robustness in Speaker Verification

In the field of speaker verification, session or channel variability poses a significant challenge. While many contemporary methods aim to disentangle session information from speaker embeddings, we introduce a novel approach using an additional embedding to represent the session information. This is achieved by training an auxiliary network appended to the speaker embedding extractor which remains fixed in this training process. This results in two similarity scores: one for the speakers information and one for the session information. The latter score acts as a compensator for the former that might be skewed due to session variations. Our extensive experiments demonstrate that session information can be effectively compensated without retraining of the embedding extractor

Remineralization Property of an Orthodontic Primer Containing a Bioactive Glass with Silver and Zinc

White spot lesions (WSLs) are irreversible damages in orthodontic treatment due to excessive etching or demineralization by microorganisms. In this study, we conducted a mechanical and cell viability test to examine the antibacterial properties of 0.2% and 1% bioactive glass (BAG) and silver-doped and zinc-doped BAGs in a primer and evaluated their clinical applicability to prevent WSLs. The microhardness statistically significantly increased in the adhesive-containing BAG, while the other samples showed no statistically significant difference compared with the control group. The shear bond strength of all samples increased compared with that of the control group. The cell viability of the control and sample groups was similar within 24 h, but decreased slightly over 48 h. All samples showed antibacterial properties. Regarding remineralization property, the group containing 0.2% of the samples showed remineralization properties compared with the control group, but was not statistically significant; further, the group containing 1% of the samples showed a significant difference compared with the control group. Among them, the orthodontic bonding primer containing 1% silver-doped BAG showed the highest remineralization property. The new orthodontic bonding primer used in this study showed an antimicrobial effect, chemical remineralization effect, and WSL prevention as well as clinically applicable properties, both physically and biologically

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

Acute Kidney Injury due to Menstruation-related Disseminated Intravascular Coagulation in an Adenomyosis Patient: A Case Report

The authors report a case of acute kidney injury (AKI) resulting from menstruation-related disseminated intravascular coagulation (DIC) in an adenomyosis patient. A 40-yr-old woman who had received gonadotropin for ovulation induction therapy presented with anuria and an elevated serum creatinine level. Her medical history showed primary infertility with diffuse adenomyosis. On admission, her pregnancy test was negative and her menstrual cycle had started 1 day previously. Laboratory data were consistent with DIC, and it was believed to be related to myometrial injury resulting from heavy intramyometrial menstrual flow. Gonadotropin is considered to play an important role in the development of fulminant DIC. This rare case suggests that physicians should be aware that gonadotropin may provoke fulminant DIC in women with adenomyosis