Look, Listen and Learn - A Multimodal LSTM for Speaker Identification

TL;DR

This paper introduces a multimodal LSTM that integrates visual and auditory data for speaker identification, improving robustness and accuracy in challenging video scenarios.

Contribution

The novel multimodal LSTM architecture models cross-modal temporal dependencies, enhancing speaker identification performance over existing methods.

Findings

Outperforms state-of-the-art systems on The Big Bang Theory dataset.

Shows increased robustness to content quality degradations and distractors.

Achieves lower false alarm rates and higher recognition accuracy.

Abstract

Speaker identification refers to the task of localizing the face of a person who has the same identity as the ongoing voice in a video. This task not only requires collective perception over both visual and auditory signals, the robustness to handle severe quality degradations and unconstrained content variations are also indispensable. In this paper, we describe a novel multimodal Long Short-Term Memory (LSTM) architecture which seamlessly unifies both visual and auditory modalities from the beginning of each sequence input. The key idea is to extend the conventional LSTM by not only sharing weights across time steps, but also sharing weights across modalities. We show that modeling the temporal dependency across face and voice can significantly improve the robustness to content quality degradations and variations. We also found that our multimodal LSTM is robustness to distractors, namely the non-speaking identities. We applied our multimodal LSTM to The Big Bang Theory dataset and showed that our system outperforms the state-of-the-art systems in speaker identification with lower false alarm rate and higher recognition accuracy.