Attention-Based Models for Text-Dependent Speaker Verification

ABSTRACT Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence. In this paper, we analyze the usage of attention mechanisms to the problem of sequence summarization in our end-to-end textdependent speaker recognition system. We explore different topologies and their variants of the attention layer, and compare different pooling methods on the attention weights. Ultimately, we show that attention-based models can improves the Equal Error Rate (EER) of our speaker verification system by relatively 14% compared to our non-attention LSTM baseline model. Index Terms-- Attention-based model, sequence summarization, speaker recognition, pooling, LSTM 1. INTRODUCTION Speaker verification (SV) is the process of verifying, based on a set of reference enrollment utterances, whether an verification utterance belongs to a known speaker.