16th Annual Conference of the International Speech Communication Association

Dresden, Germany
September 6-10, 2015

Locally-Connected and Convolutional Neural Networks for Small Footprint Speaker Recognition

Yu-hsin Chen, Ignacio Lopez-Moreno, Tara N. Sainath, Mirkó Visontai, Raziel Alvarez, Carolina Parada

Google, USA

This work compares the performance of deep Locally-Connected Networks (LCN) and Convolutional Neural Networks (CNN) for text-dependent speaker recognition. These topologies model the local time-frequency correlations of the speech signal better, using only a fraction of the number of parameters of a fully connected Deep Neural Network (DNN) used in previous works. We show that both a LCN and CNN can reduce the total model footprint to 30% of the original size compared to a baseline fully-connected DNN, with minimal impact in performance or latency. In addition, when matching parameters, the LCN improves speaker verification performance, as measured by equal error rate (EER), by 8% relative over the baseline without increasing model size or computation. Similarly, a CNN improves EER by 10% relative over the baseline for the same model size but with increased computation.

Full Paper

Bibliographic reference.  Chen, Yu-hsin / Lopez-Moreno, Ignacio / Sainath, Tara N. / Visontai, Mirkó / Alvarez, Raziel / Parada, Carolina (2015): "Locally-connected and convolutional neural networks for small footprint speaker recognition", In INTERSPEECH-2015, 1136-1140.