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This work projects the importance of phonetic match between train and test session for a text-independent framework under limited test data condition. The robustness of text-independent speaker verification (SV) tends to fall down with the reduction of the amount of speech involved. From a deployable application oriented system point of view, the amount of speech involved, is expected to be less to...
Auto-Associative Neural Network (AANN) is a fully connected feed-forward neural network, trained to reconstruct its input at its output through a hidden compression layer. AANNs are used to model speakers in speaker verification, where a speaker-specific AANN model is obtained by adapting (or retraining) the Universal Background Model (UBM) AANN, an AANN trained on multiple held out speakers, using...
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