3260 papers • 126 benchmarks • 313 datasets
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A data-driven, integrated approach to speaker verification, which maps a test utterance and a few reference utterances directly to a single score for verification and jointly optimizes the system's components using the same evaluation protocol and metric as at test time.
This paper analyzes the usage of attention mechanisms to the problem of sequence summarization in the authors' end-to-end text-dependent speaker recognition system and shows that attention-based models can improves the Equal Error Rate (EER) of the speaker verification system by relatively 14% compared to their non-attention LSTM baseline model.
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