2025/11/11 by Lu Gan, Xi Li, Gan, Lu +1
Computer Science · #FOS: Computer and information sciences #Natural Language Processing Techniques #Sound (cs.SD) #Speech Recognition and Synthesis #Speech and Audio Processing
paper · pdf · doi:10.48550/arxiv.2511.07821
openalex publication_date 2025/11/11 · openalex created_date 2025/11/13 · openalex updated_date 2026/07/28
The development of high-performance, on-device keyword spotting (KWS) systems for ultra-low-power hardware is critically constrained by the scarcity of specialized, multi-command training datasets. Traditional data collection through human recording is costly, slow, and lacks scalability. This paper introduces SYNTTS-COMMANDS, a novel, multilingual voice command dataset entirely generated using state-of-the-art Text-to-Speech (TTS) synthesis. By leveraging the CosyVoice 2 model and speaker embeddings from public corpora, we created a scalable collection of English and Chinese commands. Extensive benchmarking across a range of efficient acoustic models demonstrates that our synthetic dataset enables exceptional accuracy, achieving up to 99.5% on English and 98% on Chinese command recognition. These results robustly validate that synthetic speech can effectively replace human-recorded audio for training KWS classifiers. Our work directly addresses the data bottleneck in TinyML, providing a practical, scalable foundation for building private, low-latency, and energy-efficient voice interfaces on resource-constrained edge devices. The dataset and source code are publicly available at https://github.com/lugan113/SynTTS-Commands-Official.