SynTTS-Commands: A Public Dataset for On-Device KWS via TTS-Synthesized Multilingual Speech

TL;DR

This paper presents SynTTS-Commands, a multilingual synthetic speech dataset generated via TTS for training on-device keyword spotting systems, achieving high accuracy and addressing data scarcity in low-power voice interfaces.

Contribution

Introduces a scalable, synthetic multilingual dataset for on-device KWS, validated by high recognition accuracy, reducing reliance on costly human recordings.

Findings

Achieved up to 99.5% accuracy on English commands

Achieved up to 98% accuracy on Chinese commands

Validated synthetic data as effective for training KWS models

Abstract

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.