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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics ... Röder, Frank; Eppe, Manfred; Wermter, Stefan. - : arXiv, 2022 BASE Show details 2 Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ... Lakomkin, Egor; Zamani, Mohammad Ali; Weber, Cornelius; Magg, Sven; Wermter, Stefan. - : arXiv, 2019 Abstract: Previous work on emotion recognition demonstrated a synergistic effect of combining several modalities such as auditory, visual, and transcribed text to estimate the affective state of a speaker. Among these, the linguistic modality is crucial for the evaluation of an expressed emotion. However, manually transcribed spoken text cannot be given as input to a system practically. 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. In this paper, we propose a method of integrating an automatic speech recognition (ASR) output with a character-level recurrent neural network for sentiment recognition. In addition, we conduct several experiments investigating sentiment recognition for human-robot interaction in a noise-realistic scenario which is challenging for the ASR systems. We quantify the ... : Accepted at the 2019 International Conference on Robotics and Automation (ICRA) will be held on May 20-24, 2019 in Montreal, Canada ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG; Sound cs.SD URL: https://dx.doi.org/10.48550/arxiv.1902.11245 https://arxiv.org/abs/1902.11245 BASE Hide details

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