SpokeN-100: A Cross-Lingual Benchmarking Dataset for The Classification of Spoken Numbers in Different Languages
CoRR(2024)
摘要
Benchmarking plays a pivotal role in assessing and enhancing the performance
of compact deep learning models designed for execution on resource-constrained
devices, such as microcontrollers. Our study introduces a novel, entirely
artificially generated benchmarking dataset tailored for speech recognition,
representing a core challenge in the field of tiny deep learning. SpokeN-100
consists of spoken numbers from 0 to 99 spoken by 32 different speakers in four
different languages, namely English, Mandarin, German and French, resulting in
12,800 audio samples. We determine auditory features and use UMAP (Uniform
Manifold Approximation and Projection for Dimension Reduction) as a
dimensionality reduction method to show the diversity and richness of the
dataset. To highlight the use case of the dataset, we introduce two benchmark
tasks: given an audio sample, classify (i) the used language and/or (ii) the
spoken number. We optimized state-of-the-art deep neural networks and performed
an evolutionary neural architecture search to find tiny architectures optimized
for the 32-bit ARM Cortex-M4 nRF52840 microcontroller. Our results represent
the first benchmark data achieved for SpokeN-100.
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