77 research outputs found
2D2D HILIC-ELSD/UPLC-Q-TOF-MS Method for Acquiring Phospholipid Profiles and the Application in Caenorhabditis elegans
Phospholipids are the main constituent of cellular membranes and have recently been identified to have diagnostic value as biomarkers for many diseases. Accordingly, much emphasis is now laid on developing optimal analytical techniques for the phospholipid profiles of various biological samples. In the present study, different classes of phospholipids are first separated by optimized hydrophilic interaction chromatography with evaporative light scattering detector (HILIC-ELSD). The phospholipids in each class are then identified by ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS). Validation results confirm that this approach meets the requirements of quantitative analysis. Finally, the approach is adopted to analyze the phospholipid profiles in Caenorhabditis elegans. A total of 111 phospholipid species is identified according to the mass fragments. Major fatty acyl chains in phospholipids are found to be formed by oleic acid (C18:1), arachidonic acid (C20:4), and eicosapentaenoic acid (C20:5). Overall, this study improves current knowledge on analytical techniques of the phospholipid composition in C. elegans and provides a basis for future lipidomics research. Practical applications: Phospholipids reportedly play a crucial role in the development of many diseases. Until now, only a small portion of phospholipids in Caenorhabditis elegans has been reported by using one-dimensional analysis strategy. The offline 2D2D liquid chromatography method developed in this study identifies 111 phospholipid species in Caenorhabditis elegans. The obtained phospholipid profiles complement the lipid database of Caenorhabditis elegans. The study also provides the basis for the future development of a 2D online approach
Self-Supervised Training of Speaker Encoder with Multi-Modal Diverse Positive Pairs
We study a novel neural architecture and its training strategies of speaker
encoder for speaker recognition without using any identity labels. The speaker
encoder is trained to extract a fixed-size speaker embedding from a spoken
utterance of various length. Contrastive learning is a typical self-supervised
learning technique. However, the quality of the speaker encoder depends very
much on the sampling strategy of positive and negative pairs. It is common that
we sample a positive pair of segments from the same utterance. Unfortunately,
such poor-man's positive pairs (PPP) lack necessary diversity for the training
of a robust encoder. In this work, we propose a multi-modal contrastive
learning technique with novel sampling strategies. By cross-referencing between
speech and face data, we study a method that finds diverse positive pairs (DPP)
for contrastive learning, thus improving the robustness of the speaker encoder.
We train the speaker encoder on the VoxCeleb2 dataset without any speaker
labels, and achieve an equal error rate (EER) of 2.89\%, 3.17\% and 6.27\%
under the proposed progressive clustering strategy, and an EER of 1.44\%,
1.77\% and 3.27\% under the two-stage learning strategy with pseudo labels, on
the three test sets of VoxCeleb1. This novel solution outperforms the
state-of-the-art self-supervised learning methods by a large margin, at the
same time, achieves comparable results with the supervised learning
counterpart. We also evaluate our self-supervised learning technique on LRS2
and LRW datasets, where the speaker information is unknown. All experiments
suggest that the proposed neural architecture and sampling strategies are
robust across datasets.Comment: 13 page
Self-supervised Speaker Recognition with Loss-gated Learning
In self-supervised learning for speaker recognition, pseudo labels are useful
as the supervision signals. It is a known fact that a speaker recognition model
doesn't always benefit from pseudo labels due to their unreliability. In this
work, we observe that a speaker recognition network tends to model the data
with reliable labels faster than those with unreliable labels. This motivates
us to study a loss-gated learning (LGL) strategy, which extracts the reliable
labels through the fitting ability of the neural network during training. With
the proposed LGL, our speaker recognition model obtains a performance
gain over the system without it. Further, the proposed self-supervised speaker
recognition with LGL trained on the VoxCeleb2 dataset without any labels
achieves an equal error rate of on the VoxCeleb1 original test set.
Code has been made available at:
https://github.com/TaoRuijie/Loss-Gated-Learning.Comment: 5 pages, 3 figure
Emphasized Non-Target Speaker Knowledge in Knowledge Distillation for Automatic Speaker Verification
Knowledge distillation (KD) is used to enhance automatic speaker verification
performance by ensuring consistency between large teacher networks and
lightweight student networks at the embedding level or label level. However,
the conventional label-level KD overlooks the significant knowledge from
non-target speakers, particularly their classification probabilities, which can
be crucial for automatic speaker verification. In this paper, we first
demonstrate that leveraging a larger number of training non-target speakers
improves the performance of automatic speaker verification models. Inspired by
this finding about the importance of non-target speakers' knowledge, we
modified the conventional label-level KD by disentangling and emphasizing the
classification probabilities of non-target speakers during knowledge
distillation. The proposed method is applied to three different student model
architectures and achieves an average of 13.67% improvement in EER on the
VoxCeleb dataset compared to embedding-level and conventional label-level KD
methods.Comment: Submitted to ICASSP 202
USED: Universal Speaker Extraction and Diarization
Speaker extraction and diarization are two crucial enabling techniques for
speech applications. Speaker extraction aims to extract a target speaker's
voice from a multi-talk mixture, while speaker diarization demarcates speech
segments by speaker, identifying `who spoke when'. The previous studies have
typically treated the two tasks independently. However, the two tasks share a
similar objective, that is to disentangle the speakers in the spectral domain
for the former but in the temporal domain for the latter. It is logical to
believe that the speaker turns obtained from speaker diarization can benefit
speaker extraction, while the extracted speech offers more accurate speaker
turns than the mixture speech. In this paper, we propose a unified framework
called Universal Speaker Extraction and Diarization (USED). We extend the
existing speaker extraction model to simultaneously extract the waveforms of
all speakers. We also employ a scenario-aware differentiated loss function to
address the problem of sparsely overlapped speech in real-world conversations.
We show that the USED model significantly outperforms the baselines for both
speaker extraction and diarization tasks, in both highly overlapped and
sparsely overlapped scenarios. Audio samples are available at
https://ajyy.github.io/demo/USED/.Comment: Submitted to ICASSP 202
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