Analyzing Continuous-Time and Sentence-Level Annotations for Speech Emotion Recognition Article Swipe

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Luz Martinez-Lucas , Wei-Cheng Lin , Carlos Busso ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.1109/taffc.2024.3372380

The emotional content of several databases are annotated with continuous-time (CT) annotations, providing traces with frame-by-frame scores describing the instantaneous value of an emotional attribute. However, having a single score describing the global emotion of a short segment is more convenient for several emotion recognition formulations. A common approach is to derive sentence-level (SL) labels from CT annotations by aggregating the values of the emotional traces across time and annotators. How similar are these aggregated SL labels from labels originally collected at the sentence level? The release of the MSP-Podcast (SL annotations) and MSP-Conversation (CT annotations) corpora provides the resources to explore the validity of aggregating SL labels from CT annotations. There are 2,884 speech segments that belong to both corpora. Using this set, this study (1) compares both types of annotations using statistical metrics, (2) evaluates their inter-evaluator agreements, and (3) explores the effect of these SL labels on speech emotion recognition (SER) tasks. The analysis reveals benefits of using SL labels derived from CT annotations in the estimation of valence. This analysis also provides insights on how the two types of labels differ and how that could affect a model.

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Computer Science

Artificial Intelligence

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Speech recognition Emotion recognition Sentence Natural language processing Computer science Artificial intelligence Psychology

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/taffc.2024.3372380
PDF: https://ieeexplore.ieee.org/ielx7/5165369/5520654/10457571.pdf
OA Status: hybrid
Cited By: 10
References: 63
Related Works: 10
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DOI: https://doi.org/10.1109/taffc.2024.3372380

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Title: Analyzing Continuous-Time and Sentence-Level Annotations for Speech Emotion Recognition

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Language: en

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Publication year: 2024

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Publication date: 2024-03-01

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Authors: Luz Martinez-Lucas, Wei-Cheng Lin, Carlos Busso

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Landing page: https://doi.org/10.1109/taffc.2024.3372380

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Concepts: Speech recognition, Emotion recognition, Sentence, Natural language processing, Computer science, Artificial intelligence, Psychology

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Cited by: 10

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abstract_inverted_index.estimation	175
abstract_inverted_index.originally	83
abstract_inverted_index.MSP-Podcast	93
abstract_inverted_index.aggregating	63, 109
abstract_inverted_index.agreements,	143
abstract_inverted_index.annotations	61, 135, 172
abstract_inverted_index.annotators.	73
abstract_inverted_index.recognition	46
abstract_inverted_index.statistical	137
abstract_inverted_index.annotations)	95, 99
abstract_inverted_index.annotations,	13
abstract_inverted_index.annotations.	114
abstract_inverted_index.formulations.	47
abstract_inverted_index.instantaneous	21
abstract_inverted_index.frame-by-frame	17
abstract_inverted_index.inter-evaluator	142
abstract_inverted_index.recognition</i>	158
abstract_inverted_index.MSP-Conversation	97
abstract_inverted_index.xmlns:mml="http://www.w3.org/1998/Math/MathML"	10, 55, 155
abstract_inverted_index.xmlns:xlink="http://www.w3.org/1999/xlink">speech	156
abstract_inverted_index.xmlns:xlink="http://www.w3.org/1999/xlink">sentence-level</i>	56
abstract_inverted_index.xmlns:xlink="http://www.w3.org/1999/xlink">continuous-time</i>	11
cited_by_percentile_year.max	98
cited_by_percentile_year.min	97
countries_distinct_count	1
institutions_distinct_count	3
citation_normalized_percentile.value	0.96919878
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	True