DNN and i-vector combined method for speaker recognition on multi-variability environments

The article deals with the compensation of variability in Automatic Speaker Verification systems in scenarios where the variability conditions due to utterance duration, reverberation and environmental noise are simultaneously present. We introduce a new representation of the speaker’s discriminativ...

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Bibliographic Details
Published in:International journal of speech technology 2021-06, Vol.24 (2), p.409-418
Main Authors: Reyes-Díaz, Flavio J., Hernández-Sierra, Gabriel, de Lara, José R. Calvo
Format: Article
Language:English
Subjects:
Artificial Intelligence
Artificial neural networks
Background noise
Deep learning
Engineering
Error reduction
Noise
Representations
Reverberation
Signal,Image and Speech Processing
Social Sciences
Speaker identification
Speech recognition
Verification
Voice recognition
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Summary:The article deals with the compensation of variability in Automatic Speaker Verification systems in scenarios where the variability conditions due to utterance duration, reverberation and environmental noise are simultaneously present. We introduce a new representation of the speaker’s discriminative information, based on the use of a deep neural network trained discriminatively for speaker classification and i-vector representation. The proposed representation allows us to increase the verification performance by reducing the error between 2.5 and 7.9 % for all variability conditions compared to baseline systems. We also analyze the speaker verification system robustness based on interquartile range, obtaining a 1.19 times improvement compared to baselines evaluated.
ISSN:1381-2416
1572-8110
DOI:10.1007/s10772-021-09796-1