Incorporating End-to-End Speech Recognition Models for Sentiment Analysis
| Autor: | Mohammad Ali Zamani, Egor Lakomkin, Stefan Wermter, Sven Magg, Cornelius Weber |
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| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Computer Science - Machine Learning Computer Science - Computation and Language Modality (human–computer interaction) Computer science Speech recognition Sentiment analysis 020206 networking & telecommunications 02 engineering and technology Computer Science - Sound Machine Learning (cs.LG) Task (project management) Recurrent neural network Transcription (linguistics) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering Expressed emotion 020201 artificial intelligence & image processing Set (psychology) Computation and Language (cs.CL) Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | ICRA 2019 International Conference on Robotics and Automation (ICRA) |
| DOI: | 10.1109/icra.2019.8794468 |
| Popis: | 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 improvement compared to using only the acoustic modality in sentiment recognition. We demonstrate the effectiveness of this approach on the Multimodal Corpus of Sentiment Intensity (MOSI) by achieving 73,6% accuracy in a binary sentiment classification task, exceeding previously reported results that use only acoustic input. In addition, we set a new state-of-the-art performance on the MOSI dataset (80.4% accuracy, 2% absolute improvement). Accepted at the 2019 International Conference on Robotics and Automation (ICRA) will be held on May 20-24, 2019 in Montreal, Canada |
| Databáze: | OpenAIRE |
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