# Enhancing Voice Wake-Up for Dysarthria: Mandarin Dysarthria Speech Corpus Release and Customized System Design

arXiv: https://arxiv.org/abs/2406.10304 · HTML: https://mlsanity.com/paper/2406.10304/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 14 of 14)

- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [WeNet: Production oriented Streaming and Non-streaming End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2102.01547/) (2021)
- [Efficient keyword spotting using dilated convolutions and gating](https://mlsanity.com/paper/1811.07684/) (2018)
- [Attention-based End-to-End Models for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1803.10916/) (2018)
- [Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1705.02411/) (2017)
- [EasyCall corpus: a dysarthric speech dataset](https://mlsanity.com/paper/2104.02542/) (2021)
- [Towards Data-efficient Modeling for Wake Word Spotting](https://mlsanity.com/paper/2010.06659/) (2020)
- [CDSD: Chinese Dysarthria Speech Database](https://mlsanity.com/paper/2310.15930/) (2023)
- [WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit](https://mlsanity.com/paper/2210.16743/) (2022)
- [Data augmentation enhanced speaker enrollment for text-dependent speaker verification](https://mlsanity.com/paper/2007.08004/) (2020)
- [Dual-Attention Neural Transducers for Efficient Wake Word Spotting in Speech Recognition](https://mlsanity.com/paper/2304.01905/) (2023)
- [The DKU Post-Challenge Audio-Visual Wake Word Spotting System for the 2021 MISP Challenge: Deep Analysis](https://mlsanity.com/paper/2303.02348/) (2023)

## Built on by (most cited first, 4 of 4)

- [Roadmap towards Superhuman Speech Understanding using Large Language Models](https://mlsanity.com/paper/2410.13268/) (2024)
- [TaigiSpeech: A Low-Resource Real-World Speech Intent Dataset and Preliminary Results with Scalable Data Mining In-the-Wild](https://mlsanity.com/paper/2603.21478/) (2026)
- [Phonological Subspace Collapse Is Aetiology-Specific and Cross-Lingually Stable: Evidence from 3,374 Speakers](https://mlsanity.com/paper/2604.21706/) (2026)
- [Analyzing Speech Condition Effects in Dysarthric ASR: A Layer-wise Probing Study](https://mlsanity.com/paper/2608.01865/) (2026)

## Abstract (arXiv)

Smart home technology has gained widespread adoption, facilitating effortless control of devices through voice commands. However, individuals with dysarthria, a motor speech disorder, face challenges due to the variability of their speech. This paper addresses the wake-up word spotting (WWS) task for dysarthric individuals, aiming to integrate them into real-world applications. To support this, we release the open-source Mandarin Dysarthria Speech Corpus (MDSC), a dataset designed for dysarthric individuals in home environments. MDSC encompasses information on age, gender, disease types, and intelligibility evaluations. Furthermore, we perform comprehensive experimental analysis on MDSC, highlighting the challenges encountered. We also develop a customized dysarthria WWS system that showcases robustness in handling intelligibility and achieving exceptional performance. MDSC will be released on https://www.aishelltech.com/AISHELL_6B.