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Found 18 papers, 0 papers with code
Inspecting Spoken Language Understanding from Kids for Basic Math Learning at Home
Enriching the quality of early childhood education with interactive math learning at home systems, empowered by recent advances in conversational AI technologies, is slowly becoming a reality.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Position Matters! Empirical Study of Order Effect in Knowledge-grounded Dialogue
With the power of large pretrained language models, various research works have integrated knowledge into dialogue systems.
End-to-End Evaluation of a Spoken Dialogue System for Learning Basic Mathematics
The advances in language-based Artificial Intelligence (AI) technologies applied to build educational applications can present AI for social-good opportunities with a broader positive impact.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
NLU for Game-based Learning in Real: Initial Evaluations
Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings.
Data Augmentation with Paraphrase Generation and Entity Extraction for Multimodal Dialogue System
Contextually aware intelligent agents are often required to understand the users and their surroundings in real-time.
Semi-supervised Interactive Intent Labeling
Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS.
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
Unobtrusive and Multimodal Approach for Behavioral Engagement Detection of Students
We propose a multimodal approach for detection of students' behavioral engagement states (i. e., On-Task vs. Off-Task), based on three unobtrusive modalities: Appearance, Context-Performance, and Mouse.
Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data
To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study.
The Importance of Socio-Cultural Differences for Annotating and Detecting the Affective States of Students
One open question in annotating affective data for affect detection is whether the labelers (i. e., human experts) need to be socio-culturally similar to the students being labeled, as this impacts the cost feasibility of obtaining the labels.
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.
Named Entity Recognition on Twitter for Turkish using Semi-supervised Learning with Word Embeddings
We made use of these obtained word embeddings, together with language independent features that are engineered to work better on informal text types, for generating a Turkish NER system on microblog texts.
