1 |
Incorporating End-to-End Speech Recognition Models for Sentiment Analysis ...
|
|
|
|
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
|
|
2 |
GradAscent at EmoInt-2017: Character- and Word-Level Recurrent Neural Network Models for Tweet Emotion Intensity Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|