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Updated: Aug 1, 2026
07:21
The Emotional Stroop Task: Assessing Cognitive Performance under Exposure to Emotional Content
Published on: June 29, 2016
ASR for emotional speech: clarifying the issues and enhancing performance
T Athanaselis1, S Bakamidis, I Dologlou
1Department of Speech Technology, Institute for Language and Speech Processing (ILSP), Artemidos 6 & Epidavrou, GR-151 25 Maroussi, Athens, Greece. tathana@ilsp.gr
Summary
Recognizing emotional speech is challenging. Enhancing language models with more emotional content significantly improves the recognition of spontaneous emotional speech by approximately 20%.
Area of Science:
- Speech processing
- Computational linguistics
- Affective computing
Background:
- Recognizing verbal content in emotional speech is a known challenge with low reported success rates.
- Prosody aids recognition in acted emotions, but its effectiveness in spontaneous speech remains unproven.
Purpose of the Study:
- To investigate methods for improving the recognition rate of spontaneous emotional speech.
- To determine if language models incorporating more emotional data can enhance speech emotion recognition.
Main Methods:
- Adapted the British National Corpus (BNC) by incorporating an emotional lexicon to identify and increase the representation of emotional utterances.
- Developed and evaluated a language model based on this augmented corpus for spontaneous emotional speech recognition.
Main Results:
- The language model utilizing increased emotional utterance representation improved recognition rates by approximately 20%.
- This demonstrates the effectiveness of enhanced language models for spontaneous emotional speech.
Conclusions:
- Increasing the representation of emotional utterances in language models is a viable strategy to improve spontaneous emotional speech recognition.
- The findings suggest a practical approach to enhance the performance of speech emotion recognition systems.
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