2 research outputs found
Speaker Embeddings as Individuality Proxy for Voice Stress Detection
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.Comment: 5 pages, 2 figures. Accepted at Interspeech 202
BYOL-S: Learning Self-supervised Speech Representations by Bootstrapping
Methods for extracting audio and speech features have been studied since
pioneering work on spectrum analysis decades ago. Recent efforts are guided by
the ambition to develop general-purpose audio representations. For example,
deep neural networks can extract optimal embeddings if they are trained on
large audio datasets. This work extends existing methods based on
self-supervised learning by bootstrapping, proposes various encoder
architectures, and explores the effects of using different pre-training
datasets. Lastly, we present a novel training framework to come up with a
hybrid audio representation, which combines handcrafted and data-driven learned
audio features. All the proposed representations were evaluated within the HEAR
NeurIPS 2021 challenge for auditory scene classification and timestamp
detection tasks. Our results indicate that the hybrid model with a
convolutional transformer as the encoder yields superior performance in most
HEAR challenge tasks.Comment: Submitted to HEAR-PMLR 202