Abstract
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.
Abstract (translated)
基准测试在评估和增强资源受限设备上设计的紧凑型深度学习模型的性能中发挥着重要作用，例如微控制器。我们的研究介绍了一个新的、完全人工生成的基准测试数据集，专门针对语音识别，代表了该领域中最小的深度学习挑战。SpokeN-100 包括来自 0 到 99 的语音数字，由 32 名不同的说话者用英语、普通话、德语和法语讲述了，共产生 12,800 个音频样本。我们确定音频特征，并使用 UMAP（统一曼哈顿近似和投影降维）作为降维方法，以展示数据集的多样性和丰富性。为了突出该数据集的使用案例，我们引入了两个基准任务：给定一个音频样本，分类（i）使用的语言，（ii）说话的数字。我们优化了最先进的深度神经网络，并进行了进化神经架构搜索，以找到针对 32 位 ARM Cortex-M4 nRF52840 微控制器的最佳架构。我们的结果代表了 SpokeN-100 第一个基准数据。
URL
https://arxiv.org/abs/2403.09753
