# Investigation of Data Augmentation Techniques for Disordered Speech Recognition arXiv: https://arxiv.org/abs/2201.05562 ยท HTML: https://mlsanity.com/paper/2201.05562/ ## Key facts - Cited by 27 papers in the map (data through 14 Oct 2026). - Builds on 9 of its 45 references that are in the map. - First posted on arXiv 14 Jan 2022. ## Builds on (most cited first, 9 of 9) - [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019) - [RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation](https://mlsanity.com/paper/1905.03072/) (2019) - [Deep Scattering Spectrum](https://mlsanity.com/paper/1304.6763/) (2013) - [The Microsoft 2017 Conversational Speech Recognition System](https://mlsanity.com/paper/1708.06073/) (2017) - [Learning Hidden Unit Contributions for Unsupervised Acoustic Model Adaptation](https://mlsanity.com/paper/1601.02828/) (2016) - [English Conversational Telephone Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1703.02136/) (2017) - [Back-Translation-Style Data Augmentation for End-to-End ASR](https://mlsanity.com/paper/1807.10893/) (2018) - [Simulating dysarthric speech for training data augmentation in clinical speech applications](https://mlsanity.com/paper/1804.10325/) (2018) - [English Broadcast News Speech Recognition by Humans and Machines](https://mlsanity.com/paper/1904.13258/) (2019) ## Built on by (most cited first, 27 of 27) - [SpeechSplit 2.0: Unsupervised speech disentanglement for voice conversion Without tuning autoencoder Bottlenecks](https://mlsanity.com/paper/2203.14156/) (2022) - [Speaker Adaptation Using Spectro-Temporal Deep Features for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2202.10290/) (2022) - [Recent Progress in the CUHK Dysarthric Speech Recognition System](https://mlsanity.com/paper/2201.05845/) (2022) - [Personalized Adversarial Data Augmentation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2205.06445/) (2022) - [Exploring Self-supervised Pre-trained ASR Models For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2302.14564/) (2023) - [Conformer Based Elderly Speech Recognition System for Alzheimer's Disease Detection](https://mlsanity.com/paper/2206.13232/) (2022) - [Exploiting Cross Domain Acoustic-to-articulatory Inverted Features For Disordered Speech Recognition](https://mlsanity.com/paper/2203.10274/) (2022) - [Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2407.13782/) (2024) - [Cross-lingual Dysarthria Severity Classification for English, Korean, and Tamil](https://mlsanity.com/paper/2209.12942/) (2022) - [Use of Speech Impairment Severity for Dysarthric Speech Recognition](https://mlsanity.com/paper/2305.10659/) (2023) - [Exploiting Cross-domain And Cross-Lingual Ultrasound Tongue Imaging Features For Elderly And Dysarthric Speech Recognition](https://mlsanity.com/paper/2206.07327/) (2022) - [Accurate synthesis of Dysarthric Speech for ASR data augmentation](https://mlsanity.com/paper/2308.08438/) (2023) - [DuTa-VC: A Duration-aware Typical-to-atypical Voice Conversion Approach with Diffusion Probabilistic Model](https://mlsanity.com/paper/2306.10588/) (2023) - [On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition](https://mlsanity.com/paper/2203.14593/) (2022) - [Hyper-parameter Adaptation of Conformer ASR Systems for Elderly and Dysarthric Speech Recognition](https://mlsanity.com/paper/2306.15265/) (2023) - [No Pitch Left Behind: Addressing Gender Unbalance in Automatic Speech Recognition through Pitch Manipulation](https://mlsanity.com/paper/2310.06590/) (2023) - [Debiased Automatic Speech Recognition for Dysarthric Speech via Sample Reweighting with Sample Affinity Test](https://mlsanity.com/paper/2305.13108/) (2023) - [Towards Inclusive ASR: Investigating Voice Conversion for Dysarthric Speech Recognition in Low-Resource Languages](https://mlsanity.com/paper/2505.14874/) (2025) - [Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation](https://mlsanity.com/paper/2407.06310/) (2024) - [Not All Errors Are Equal: Investigation of Speech Recognition Errors in Alzheimer's Disease Detection](https://mlsanity.com/paper/2412.06332/) (2024) - [Fairness in Dysarthric Speech Synthesis: Understanding Intrinsic Bias in Dysarthric Speech Cloning using F5-TTS](https://mlsanity.com/paper/2508.05102/) (2025) - [Arabic Dysarthric Speech Recognition Using Adversarial and Signal-Based Augmentation](https://mlsanity.com/paper/2306.04368/) (2023) - [On-the-fly Routing for Zero-shot MoE Speaker Adaptation of Speech Foundation Models for Dysarthric Speech Recognition](https://mlsanity.com/paper/2505.22072/) (2025) - [Controllable Dysarthric Speech Synthesis with Patient-Specific Conditioning for Speaker-Diverse ASR Augmentation](https://mlsanity.com/paper/2602.08696/) (2026) - [Huntington Disease Automatic Speech Recognition with Biomarker Supervision](https://mlsanity.com/paper/2603.11168/) (2026) - [Towards Personalized Federated Learning for Dysarthric Speech Recognition](https://mlsanity.com/paper/2606.13253/) (2026) - [Augmenting Dysarthric Speech Severity Assessment with MOS Supervision](https://mlsanity.com/paper/2606.18645/) (2026) ## Abstract (arXiv) Disordered speech recognition is a highly challenging task. The underlying neuro-motor conditions of people with speech disorders, often compounded with co-occurring physical disabilities, lead to the difficulty in collecting large quantities of speech required for system development. This paper investigates a set of data augmentation techniques for disordered speech recognition, including vocal tract length perturbation (VTLP), tempo perturbation and speed perturbation. Both normal and disordered speech were exploited in the augmentation process. Variability among impaired speakers in both the original and augmented data was modeled using learning hidden unit contributions (LHUC) based speaker adaptive training. The final speaker adapted system constructed using the UASpeech corpus and the best augmentation approach based on speed perturbation produced up to 2.92% absolute (9.3% relative) word error rate (WER) reduction over the baseline system without data augmentation, and gave an overall WER of 26.37% on the test set containing 16 dysarthric speakers.