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Speaker Embeddings as Individuality Proxy for Voice Stress Detection

arXiv 2306.05915 ↗First posted 9 Jun 2023eess.AS cs.LGcited by 0

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Abstract (arXiv)

Since the mental states of the speaker modulate speech, stress introduced by cognitive or physical loads could be detected in the voice. The existing voice stress detection benchmark has shown that the audio embeddings extracted from the Hybrid BYOL-S self-supervised model perform well. However, the benchmark only evaluates performance separately on each dataset, but does not evaluate performance across the different types of stress and different languages. Moreover, previous studies found strong individual differences in stress susceptibility. This paper presents the design and development of voice stress detection, trained on more than 100 speakers from 9 language groups and five different types of stress. We address individual variabilities in voice stress analysis by adding speaker embeddings to the hybrid BYOL-S features. The proposed method significantly improves voice stress detection performance with an input audio length of only 3-5 seconds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 9 Jun 2023.

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References · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021CvT: Introducing Convolutions to Vision Transformerscited by 947
  3. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  4. 2021SpeechBrain: A General-Purpose Speech Toolkitcited by 406
  5. 2017Generalized End-to-End Loss for Speaker Verificationcited by 293
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  7. 2021BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representationcited by 106
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  9. 2022Masked Spectrogram Modeling using Masked Autoencoders for Learning General-purpose Audio Representationcited by 52
  10. 2021SERAB: A multi-lingual benchmark for speech emotion recognitioncited by 22
  11. 2022Investigating Self-supervised Pretraining Frameworks for Pathological Speech Recognitioncited by 15
  12. 2019On the Effect of Observed Subject Biases in Apparent Personality Analysis from Audio-visual Signalscited by 11
5 more
  1. 2022BYOL-S: Learning Self-supervised Speech Representations by Bootstrappingcited by 10
  2. 2021Experimental investigation on STFT phase representations for deep learning-based dysarthric speech detectioncited by 7
  3. 2022Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Loadcited by 6
  4. 2022Efficient Speech Quality Assessment using Self-supervised Framewise Embeddingscited by 2
  5. 2023Personalized Task Load Prediction in Speech Communicationcited by 2

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