Year: 2018
Type: preprint
Abstract: With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home. Going forward, we are witnessing a confluence of vision, speech and dialog system technologies that are enabling the IVAs to learn audio-visual groundings of utterances and have conversations with users about the objects, activities and events surrounding them. As a part of the 7th Dialog System Technology Challenges (DSTC7), for Audio Visual Scene-Aware Dialog (AVSD) track, We explore `topics' of the dialog as an important contextual feature into the architecture along with explorations around multimodal Attention. We also incorporate an end-to-end audio classification ConvNet, AclNet, into our models. We present detailed analysis of the experiments and show that some of our model variations outperform the baseline system presented for this task.
Source: arXiv (Cornell University)
Authors: Kumar, Shachi H, Okur, Eda, Sahay, Saurav, Leanos, Juan Jose Alvarado, Huang, Jonathan, Nachman, Lama
Language: English
Topic: Speech and dialogue systems
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2903594499