EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech
  Emotion Detection
The advancement of text-to-speech and audio generation models necessitates
robust benchmarks for evaluating the emotional understanding capabilities of AI
systems. Current speech emotion recognition (SER) datasets often exhibit
limitations in emotional granularity, privacy concerns, or reliance on acted
portrayals. This paper introduces EmoNet-Voice, a new resource for speech
emotion detection, which includes EmoNet-Voice Big, a large-scale pre-training
dataset (featuring over 4,500 hours of speech across 11 voices, 40 emotions,
and 4 languages), and EmoNet-Voice Bench, a novel benchmark dataset with human
expert annotations. EmoNet-Voice is designed to evaluate SER models on a
fine-grained spectrum of 40 emotion categories with different levels of
intensities. Leveraging state-of-the-art voice generation, we curated synthetic
audio snippets simulating actors portraying scenes designed to evoke specific
emotions. Crucially, we conducted rigorous validation by psychology experts who
assigned perceived intensity labels. This synthetic, privacy-preserving
approach allows for the inclusion of sensitive emotional states often absent in
existing datasets. Lastly, we introduce Empathic Insight Voice models that set
a new standard in speech emotion recognition with high agreement with human
experts. Our evaluations across the current model landscape exhibit valuable
findings, such as high-arousal emotions like anger being much easier to detect
than low-arousal states like concentration.
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Christoph Schuhmann
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Christoph Schuhmann
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ChristophSchuhmann
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Robert Kaczmarczyk
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Gollam Rabby
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2025-06-20T12:29:47.779Z
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Gollam Rabby
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Felix Friedrich
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Felix Friedrich
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felfri
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Maurice Kraus
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Kourosh Nadi
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Huu Nguyen
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2025-06-23T08:15:57.375Z
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Huu Nguyen
user
huu-ontocord
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Kristian Kersting
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Sören Auer
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2025-06-20T12:30:06.624Z
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Sören Auer
user
soeren1611
EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech
  Emotion Detection
The advancement of text-to-speech and audio generation models necessitates
robust benchmarks for evaluating the emotional understanding capabilities of AI
systems. Current speech emotion recognition (SER) datasets often exhibit
limitations in emotional granularity, privacy concerns, or reliance on acted
portrayals. This paper introduces EmoNet-Voice, a new resource for speech
emotion detection, which includes EmoNet-Voice Big, a large-scale pre-training
dataset (featuring over 4,500 hours of speech across 11 voices, 40 emotions,
and 4 languages), and EmoNet-Voice Bench, a novel benchmark dataset with human
expert annotations. EmoNet-Voice is designed to evaluate SER models on a
fine-grained spectrum of 40 emotion categories with different levels of
intensities. Leveraging state-of-the-art voice generation, we curated synthetic
audio snippets simulating actors portraying scenes designed to evoke specific
emotions. Crucially, we conducted rigorous validation by psychology experts who
assigned perceived intensity labels. This synthetic, privacy-preserving
approach allows for the inclusion of sensitive emotional states often absent in
existing datasets. Lastly, we introduce Empathic Insight Voice models that set
a new standard in speech emotion recognition with high agreement with human
experts. Our evaluations across the current model landscape exhibit valuable
findings, such as high-arousal emotions like anger being much easier to detect
than low-arousal states like concentration.
EmoNet-Voice, a new resource with large pre-training and benchmark datasets, advances speech emotion recognition by offering fine-grained emotion evaluation with synthetic, privacy-preserving audio.
speech emotion recognition SER EmoNet-Voice EmoNet-Voice Big EmoNet-Voice Bench human expert annotations synthetic audio snippets psychology experts high-arousal emotions low-arousal states Empathic Insight Voice models