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Found 12 papers, 1 papers with code
CueBot: Cue-Controlled Response Generation for Assistive Interaction Usages
Conversational assistants are ubiquitous among the general population, however, these systems have not had an impact on people with disabilities, or speech and language disorders, for whom basic day-to-day communication and social interaction is a huge struggle.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Audio-Visual Understanding of Passenger Intents for In-Cabin Conversational Agents
To this end, understanding passenger intents from spoken interactions and vehicle vision systems is a crucial component for developing contextual and visually grounded conversational agents for AV.
Low Rank Fusion based Transformers for Multimodal Sequences
In this work, we experiment with modeling modality-specific sensory signals to attend to our latent multimodal emotional intentions and vice versa expressed via low-rank multimodal fusion and multimodal transformers.
Leveraging Topics and Audio Features with Multimodal Attention for Audio Visual Scene-Aware Dialog
With the recent advancements in Artificial Intelligence (AI), Intelligent Virtual Assistants (IVA) such as Alexa, Google Home, etc., have become a ubiquitous part of many homes.
Modeling Intent, Dialog Policies and Response Adaptation for Goal-Oriented Interactions
Building a machine learning driven spoken dialog system for goal-oriented interactions involves careful design of intents and data collection along with development of intent recognition models and dialog policy learning algorithms.
Exploring Context, Attention and Audio Features for Audio Visual Scene-Aware Dialog
Recent progress in visual grounding techniques and Audio Understanding are enabling machines to understand shared semantic concepts and listen to the various sensory events in the environment.
Towards Multimodal Understanding of Passenger-Vehicle Interactions in Autonomous Vehicles: Intent/Slot Recognition Utilizing Audio-Visual Data
Understanding passenger intents from spoken interactions and car's vision (both inside and outside the vehicle) are important building blocks towards developing contextual dialog systems for natural interactions in autonomous vehicles (AV).
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
Understanding passenger intents and extracting relevant slots are important building blocks towards developing contextual dialogue systems for natural interactions in autonomous vehicles (AV).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Context, Attention and Audio Feature Explorations for Audio Visual Scene-Aware Dialog
With the recent advancements in AI, Intelligent Virtual Assistants (IVA) have become a ubiquitous part of every home.
Multimodal Relational Tensor Network for Sentiment and Emotion Classification
Understanding Affect from video segments has brought researchers from the language, audio and video domains together.
