Yanpei Shi(University of Sheffield), Qiang Huang(University of Sheffield) and Thomas Hain(University of Sheffield)
Abstract:
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.