10,886 research outputs found
Constraints on absolute neutrino Majorana mass from current data
We present new constraints on the neutrino Majorana masses from the current
data of neutrinoless double beta decay and neutrino flavour mixing. With the
latest results of progresses from various isotopes, including
the recent calculations of the nuclear matrix elements, we find that the
strongest constraint of the effective Majorana neutrino mass is from the
data of the EXO-200 and KamLAND-Zen collaborations. Further
more, by combining the experimental data with the neutrino
mixing parameters from new analyses, we get the mass upper limits of neutrino
mass eigenstates and flavour eigenstates and suggest several relations among
these neutrino masses.Comment: 6 latex pages, 2 figures. Final version for publication in "The
Universe
Speaker Re-identification with Speaker Dependent Speech Enhancement
While the use of deep neural networks has significantly boosted speaker
recognition performance, it is still challenging to separate speakers in poor
acoustic environments. Here speech enhancement methods have traditionally
allowed improved performance. The recent works have shown that adapting speech
enhancement can lead to further gains. This paper introduces a novel approach
that cascades speech enhancement and speaker recognition. In the first step, a
speaker embedding vector is generated , which is used in the second step to
enhance the speech quality and re-identify the speakers. Models are trained in
an integrated framework with joint optimisation. The proposed approach is
evaluated using the Voxceleb1 dataset, which aims to assess speaker recognition
in real world situations. In addition three types of noise at different
signal-noise-ratios were added for this work. The obtained results show that
the proposed approach using speaker dependent speech enhancement can yield
better speaker recognition and speech enhancement performances than two
baselines in various noise conditions.Comment: Acceptted for presentation at Interspeech202
Robust Speaker Recognition Using Speech Enhancement And Attention Model
In this paper, a novel architecture for speaker recognition is proposed by
cascading speech enhancement and speaker processing. Its aim is to improve
speaker recognition performance when speech signals are corrupted by noise.
Instead of individually processing speech enhancement and speaker recognition,
the two modules are integrated into one framework by a joint optimisation using
deep neural networks. Furthermore, to increase robustness against noise, a
multi-stage attention mechanism is employed to highlight the speaker related
features learned from context information in time and frequency domain. To
evaluate speaker identification and verification performance of the proposed
approach, we test it on the dataset of VoxCeleb1, one of mostly used benchmark
datasets. Moreover, the robustness of our proposed approach is also tested on
VoxCeleb1 data when being corrupted by three types of interferences, general
noise, music, and babble, at different signal-to-noise ratio (SNR) levels. The
obtained results show that the proposed approach using speech enhancement and
multi-stage attention models outperforms two strong baselines not using them in
most acoustic conditions in our experiments.Comment: Acceptted by Odyssey 202
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