Archives of Acoustics, 45, 4, pp. 573–583, 2020

SpeakerNet for Cross-lingual Text-Independent Speaker Verification

Lahore College for Women University

Lahore College for Women University

Muhammad Abuzar FAHIEM
Lahore College for Women University

Lahore College for Women University

Ghousia USMAN
Lahore College for Women University

Biometrics provide an alternative to passwords and pins for authentication. The emergence of machine learning algorithms provides an easy and economical solution to authentication problems. The phases of speaker verification protocol are training, enrollment of speakers and evaluation of unknown voice. In this paper, we addressed text independent speaker verification using Siamese convolutional network. Siamese networks are twin networks with shared weights. Feature space can be learnt easily by training these networks even if similar observations are placed in proximity. Extracted features from Siamese then can be classified using difference or correlation measures. We have implemented a customized scoring scheme that utilizes Siamese’ capability of applying distance measures with the convolutional learning. Experiments made on cross language audios of multi-lingual speakers confirm the capability of our architecture to handle gender, age and language independent speaker verification. Moreover, our designed Siamese network, SpeakerNet, provided better results than the existing speaker verification approaches by decreasing the equal error rate to 0.02.
Keywords: Convolutional Neural Network; Deep learning; Siamese network; speaker verification; textindependent; binary operation; Urdu speaker recognition
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DOI: 10.24425/aoa.2020.134073