Fast Speaker Verification on Mobile Phone data using Boosted Slice Classifiers

In this work, we investigate a novel computationally efficient speaker verification (SV) system involving boosted ensembles of simple threshold-based classifiers. The system is based on a novel set of features called “slice features”. Both the system and the features were inspired by the recent success of pixel comparison-based ensemble approaches in the computer vision domain. The performance of the proposed system was evaluated through speaker verification experiments on the MOBIO corpus containing mobile phone speech, according to a challenging protocol. The system was found to perform reasonably well, compared to multiple state-of-the-art SV systems, with the benefit of significantly lower computational complexity. Its dual characteristics of good performance and computational efficiency could be important factors in the context of SV system implementation on portable devices like mobile phones.

Presented at:
IAPR IEEE International Joint Conference on Biometrics, Washington DC

 Record created 2013-12-19, last modified 2018-09-13

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