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Found 19 papers, 8 papers with code
A Sub-Layered Hierarchical Pyramidal Neural Architecture for Facial Expression Recognition
In domains where computational resources and labeled data are limited, such as in robotics, deep networks with millions of weights might not be the optimal solution.
An Ensemble with Shared Representations Based on Convolutional Networks for Continually Learning Facial Expressions
Social robots able to continually learn facial expressions could progressively improve their emotion recognition capability towards people interacting with them.
Disambiguating Affective Stimulus Associations for Robot Perception and Dialogue
In this paper, we utilize the NICO robot's appearance and capabilities to give the NICO the ability to model a coherent affective association between a perceived auditory stimulus and a temporally asynchronous emotion expression.
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural Networks
Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.
Ranked #9 on
Facial Expression Recognition (FER)
on FER+
(using extra training data)
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.
Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks
Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion.
Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples
Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans.
Improving interactive reinforcement learning: What makes a good teacher?
Interactive reinforcement learning has become an important apprenticeship approach to speed up convergence in classic reinforcement learning problems.
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos.
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis
We argue that using ground-truth transcriptions during training and evaluation phases leads to a significant discrepancy in performance compared to real-world conditions, as the spoken text has to be recognized on the fly and can contain speech recognition mistakes.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Assessing the Contribution of Semantic Congruency to Multisensory Integration and Conflict Resolution
The efficient integration of multisensory observations is a key property of the brain that yields the robust interaction with the environment.
Curriculum goal masking for continuous deep reinforcement learning
Deep reinforcement learning has recently gained a focus on problems where policy or value functions are independent of goals.
Speeding up the Hyperparameter Optimization of Deep Convolutional Neural Networks
This is independent of the underlying optimization procedure, making the approach promising for many existing hyperparameter optimization algorithms.
Discourse-Wizard: Discovering Deep Discourse Structure in your Conversation with RNNs
Spoken language understanding is one of the key factors in a dialogue system, and a context in a conversation plays an important role to understand the current utterance.
A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Dialogue act recognition is an important part of natural language understanding.
Ranked #10 on
Dialogue Act Classification
on Switchboard corpus
Conversational Analysis using Utterance-level Attention-based Bidirectional Recurrent Neural Networks
Recent approaches for dialogue act recognition have shown that context from preceding utterances is important to classify the subsequent one.
Ranked #9 on
Dialogue Act Classification
on Switchboard corpus
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks
Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community.
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning
Acoustically expressed emotions can make communication with a robot more efficient.
Reusing Neural Speech Representations for Auditory Emotion Recognition
Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models.
