29 citations · 40 across the 8 of their papers we have counts for
9 papers
Subword Regularization: An Analysis of Scalability and Generalization for End-to-End Automatic Speech Recognition
Egor Lakomkin, Jahn Heymann, Ilya Sklyar +1
Subwords are the most widely used output units in end-to-end speech recognition. They combine the best of two worlds by modeling the majority of frequent words directly and at the…
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
Egor Lakomkin, Sven Magg, Cornelius Weber +1
In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and pos…
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis
Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber +2
Previous work on emotion recognition demonstrated a synergistic effect of combining several modalities such as auditory, visual, and transcribed text to estimate the affective stat…
On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks
Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber +2
Speech emotion recognition (SER) is an important aspect of effective human-robot collaboration and received a lot of attention from the research community. For example, many neural…
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning
Egor Lakomkin, Mohammad Ali Zamani, Cornelius Weber +2
Acoustically expressed emotions can make communication with a robot more efficient. Detecting emotions like anger could provide a clue for the robot indicating unsafe/undesired sit…
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection
Egor Lakomkin, Chandrakant Bothe, Stefan Wermter
The WASSA 2017 EmoInt shared task has the goal to predict emotion intensity values of tweet messages. Given the text of a tweet and its emotion category (anger, joy, fear, and sadn…
Reusing Neural Speech Representations for Auditory Emotion Recognition
Egor Lakomkin, Cornelius Weber, Sven Magg +1
Acoustic emotion recognition aims to categorize the affective state of the speaker and is still a difficult task for machine learning models. The difficulties come from the scarcit…
Automatically augmenting an emotion dataset improves classification using audio
Egor Lakomkin, Cornelius Weber, Stefan Wermter
In this work, we tackle a problem of speech emotion classification. One of the issues in the area of affective computation is that the amount of annotated data is very limited. On…
The OMG-Emotion Behavior Dataset
Pablo Barros, Nikhil Churamani, Egor Lakomkin +3
This paper is the basis paper for the accepted IJCNN challenge One-Minute Gradual-Emotion Recognition (OMG-Emotion) by which we hope to foster long-emotion classification using neu…