Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching

• URL: http://arxiv.org/abs/2506.16127v1
• Date: Thu, 19 Jun 2025 08:24:17 GMT
• Title: Improved Intelligibility of Dysarthric Speech using Conditional Flow Matching
• Authors: Shoutrik Das, Nishant Singh, Arjun Gangwar, S Umesh,
• Abstract summary: Dysarthria is a neurological disorder that significantly impairs speech intelligibility.<n>This necessitates the development of robust dysarthric-to-regular speech conversion techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dysarthria is a neurological disorder that significantly impairs speech intelligibility, often rendering affected individuals unable to communicate effectively. This necessitates the development of robust dysarthric-to-regular speech conversion techniques. In this work, we investigate the utility and limitations of self-supervised learning (SSL) features and their quantized representations as an alternative to mel-spectrograms for speech generation. Additionally, we explore methods to mitigate speaker variability by generating clean speech in a single-speaker voice using features extracted from WavLM. To this end, we propose a fully non-autoregressive approach that leverages Conditional Flow Matching (CFM) with Diffusion Transformers to learn a direct mapping from dysarthric to clean speech. Our findings highlight the effectiveness of discrete acoustic units in improving intelligibility while achieving faster convergence compared to traditional mel-spectrogram-based approaches.

Related papers

• Adapting Foundation Speech Recognition Models to Impaired Speech: A Semantic Re-chaining Approach for Personalization of German Speech [0.562479170374811]
  Speech impairments caused by conditions such as cerebral palsy or genetic disorders pose significant challenges for automatic speech recognition systems.<n>We propose a practical and lightweight pipeline to personalize ASR models, formalizing the selection of words and enriching a small, speech-impaired dataset with semantic coherence.<n>Our approach shows promising improvements in transcription quality, demonstrating the potential to reduce communication barriers for individuals with atypical speech patterns.
  arXiv  Detail & Related papers  (2025-06-23T15:30:50Z)
• Unsupervised Rhythm and Voice Conversion of Dysarthric to Healthy Speech for ASR [18.701864254184308]
  We combine rhythm and voice conversion methods based on self-supervised speech representations to map dysarthric to typical speech.<n>We find that the proposed rhythm conversion especially improves performance for speakers of the Torgo corpus with more severe cases of dysarthria.
  arXiv  Detail & Related papers  (2025-01-17T15:39:21Z)
• Investigating the Effects of Diffusion-based Conditional Generative Speech Models Used for Speech Enhancement on Dysarthric Speech [7.787211625411271]
  We experimentally show that during the enhancement process of dysarthric speech data recorded in an ideal non-noisy environment, some of the acoustic dysarthric speech cues are lost.<n>We show that the removed acoustics cues by the enhancement models in the form of residue speech signal can provide complementary dysarthric cues when fused with the original input speech signal in the feature space.
  arXiv  Detail & Related papers  (2024-12-18T15:18:05Z)
• UNIT-DSR: Dysarthric Speech Reconstruction System Using Speech Unit Normalization [60.43992089087448]
  Dysarthric speech reconstruction systems aim to automatically convert dysarthric speech into normal-sounding speech. We propose a Unit-DSR system, which harnesses the powerful domain-adaptation capacity of HuBERT for training efficiency improvement. Compared with NED approaches, the Unit-DSR system only consists of a speech unit normalizer and a Unit HiFi-GAN vocoder, which is considerably simpler without cascaded sub-modules or auxiliary tasks.
  arXiv  Detail & Related papers  (2024-01-26T06:08:47Z)
• DDTSE: Discriminative Diffusion Model for Target Speech Extraction [62.422291953387955]
  We introduce the Discriminative Diffusion model for Target Speech Extraction (DDTSE) We apply the same forward process as diffusion models and utilize the reconstruction loss similar to discriminative methods. We devise a two-stage training strategy to emulate the inference process during model training.
  arXiv  Detail & Related papers  (2023-09-25T04:58:38Z)
• Weakly-supervised forced alignment of disfluent speech using phoneme-level modeling [10.283092375534311]
  We propose a simple and effective modification of alignment graph construction using weighted Finite State Transducers. The proposed weakly-supervised approach alleviates the need for verbatim transcription of speech disfluencies for forced alignment. Our evaluation on a corrupted version of the TIMIT test set and the UCLASS dataset shows significant improvements.
  arXiv  Detail & Related papers  (2023-05-30T09:57:36Z)
• Recent Progress in the CUHK Dysarthric Speech Recognition System [66.69024814159447]
  Disordered speech presents a wide spectrum of challenges to current data intensive deep neural networks (DNNs) based automatic speech recognition technologies. This paper presents recent research efforts at the Chinese University of Hong Kong to improve the performance of disordered speech recognition systems.
  arXiv  Detail & Related papers  (2022-01-15T13:02:40Z)
• Investigation of Data Augmentation Techniques for Disordered Speech Recognition [69.50670302435174]
  This paper investigates a set of data augmentation techniques for disordered speech recognition. Both normal and disordered speech were exploited in the augmentation process. 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 word error rate (WER)
  arXiv  Detail & Related papers  (2022-01-14T17:09:22Z)
• Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem [65.25725367771075]
  This study demonstrates, for the first time, that the synthesis-based approach can also perform well on this problem. Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols. By utilizing the synthesis model with the input of discrete symbols, after the prediction of discrete symbol sequence, each target speech could be re-synthesized.
  arXiv  Detail & Related papers  (2021-12-17T08:35:40Z)
• Pathological voice adaptation with autoencoder-based voice conversion [15.687800631199616]
  Instead of using healthy speech as a source, we customise an existing pathological speech sample to a new speaker's voice characteristics. This approach alleviates the evaluation problem one normally has when converting typical speech to pathological speech.
  arXiv  Detail & Related papers  (2021-06-15T20:38:10Z)
• A Preliminary Study of a Two-Stage Paradigm for Preserving Speaker Identity in Dysarthric Voice Conversion [50.040466658605524]
  We propose a new paradigm for maintaining speaker identity in dysarthric voice conversion (DVC) The poor quality of dysarthric speech can be greatly improved by statistical VC. But as the normal speech utterances of a dysarthria patient are nearly impossible to collect, previous work failed to recover the individuality of the patient.
  arXiv  Detail & Related papers  (2021-06-02T18:41:03Z)
• High Fidelity Speech Regeneration with Application to Speech Enhancement [96.34618212590301]
  We propose a wav-to-wav generative model for speech that can generate 24khz speech in a real-time manner. Inspired by voice conversion methods, we train to augment the speech characteristics while preserving the identity of the source.
  arXiv  Detail & Related papers  (2021-01-31T10:54:27Z)
• Improving Dysarthric Speech Intelligibility Using Cycle-consistent Adversarial Training [4.050982413149992]
  The goal of our work is to develop a model for dysarthric to healthy speech conversion using Cycle-consistent GAN. The generator is trained to transform dysarthric to healthy speech in the spectral domain, which is then converted back to speech.
  arXiv  Detail & Related papers  (2020-01-10T01:40:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.