29 citations · 40 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
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…