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arxiv: 2606.21457 · v1 · pith:ZDDACM6Fnew · submitted 2026-06-19 · 💻 cs.SD · eess.AS

DisSpeech: Low-Resource Controllable Mandarin Stuttered Speech Synthesis for ASR Augmentation

Yao Lu This is my paper

Pith reviewed 2026-06-26 12:51 UTC · model grok-4.3

classification 💻 cs.SD eess.AS
keywords stuttered speech synthesisMandarin ASRdisfluency controldiscrete speech tokensdata augmentationlow-resource speech synthesisspeech recognition
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The pith

DisSpeech generates controllable stuttered Mandarin speech from under 50 hours of data to improve ASR models on disfluent speech.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a framework for creating stuttered Mandarin speech when real examples are scarce. It adds explicit labels for repetition, prolongation, and block events, then uses a non-autoregressive model to turn text plus labels into semantic tokens before reconstructing audio with pitch and energy control. This produces synthetic data that can be added to ASR training sets. The resulting models reach lower error rates on real stuttered test speech while showing only minor drops on fluent speech. Readers would care because stuttered speech is common in everyday talk yet remains poorly handled by current recognizers.

Core claim

DisSpeech is a discrete speech token-based framework that maps text and stuttering event labels into semantic speech tokens by a non-autoregressive masked generative Transformer, followed by prosody-aware acoustic reconstruction with explicit pitch and energy modeling. Fine-tuned on less than 50 hours of Mandarin stuttered speech, the system generates controllable stuttered speech that outperforms prior synthesis methods in quality and event controllability. When used for ASR data augmentation, the synthesized speech improves multiple recognizers, reaching a state-of-the-art CER of 4.19 percent on the evaluated Mandarin stuttered speech recognition task with only slight degradation on fluent

What carries the argument

Non-autoregressive masked generative Transformer that maps text and explicit stuttering event labels to semantic speech tokens before prosody-aware acoustic reconstruction.

If this is right

  • The method outperforms earlier stuttered speech synthesis approaches in both quality and controllability of disfluency events.
  • Augmenting ASR training with the generated speech yields measurable gains on stuttered test sets for multiple models.
  • Qwen3-ASR-0.6B reaches 4.19 percent CER on the stuttered recognition task after augmentation.
  • Performance loss on fluent speech remains slight after the same augmentation step.
  • The framework operates effectively after fine-tuning on under 50 hours of target-language stuttered data.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same label-driven token approach could be tested on other languages that also lack large stuttered corpora.
  • Generated data might be mixed with real recordings in varying ratios to find the smallest augmentation volume that still lifts ASR accuracy.
  • The controllability over specific disfluency types opens the possibility of targeted training sets for particular speaker populations or clinical applications.
  • Because the pipeline separates semantic token generation from acoustic rendering, future work could swap in different acoustic models without retraining the label-to-token stage.

Load-bearing premise

That fine-tuning on less than 50 hours of real stuttered Mandarin speech produces synthetic examples realistic enough to improve ASR on actual disfluent recordings without introducing harmful artifacts.

What would settle it

Train ASR models on the same fluent data plus either DisSpeech-augmented stuttered examples or real stuttered examples, then compare character error rates on a held-out set of real Mandarin stuttered recordings.

Figures

Figures reproduced from arXiv: 2606.21457 by Yao Lu.

Figure 1
Figure 1. Figure 1: Task formulation of Mandarin stuttered speech synthesis. (a) The ob [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed Mandarin stuttered speech synthesis framework. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Illustration of the non-autoregressive masked generative Transformer used [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Mel-spectrogram comparisons between ground-truth and synthesized [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
read the original abstract

Stuttered speech recognition remains challenging, with disfluencies such as repetitions, prolongations, and blocks disrupting speech continuity and acoustic patterns. This problem is further aggravated in Mandarin scenarios by the limited availability of stuttered speech data, which makes it difficult to train robust ASR models for diverse disfluency patterns. To address this problem, this paper proposes DisSpeech, a discrete speech token-based framework for low-resource controllable Mandarin stuttered speech synthesis and ASR data augmentation. The proposed framework introduces explicit stuttering event labels to control different disfluency patterns. Text and stuttering event labels are mapped into semantic speech tokens by a non-autoregressive masked generative Transformer, followed by prosody-aware acoustic reconstruction with explicit pitch and energy modeling. With fine-tuning using less than 50 hours of Mandarin stuttered speech, DisSpeech can generate controllable stuttered speech with competitive speech quality. Experimental results show that the proposed method outperforms previous stuttered speech synthesis methods in both speech quality and event controllability. Furthermore, the synthesized stuttered speech effectively improves multiple ASR models, with Qwen3-ASR-0.6B achieving a state-of-the-art CER of 4.19% on the evaluated Mandarin stuttered speech recognition task, while causing only slight degradation on fluent speech.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes DisSpeech, a discrete speech token-based framework for low-resource controllable Mandarin stuttered speech synthesis. It uses explicit stuttering event labels mapped via a non-autoregressive masked generative Transformer, followed by prosody-aware acoustic reconstruction with pitch and energy modeling. Fine-tuned on <50 hours of Mandarin stuttered data, it claims competitive synthesis quality and controllability, outperforming prior methods, and that the generated data augments multiple ASR models to achieve SOTA CER of 4.19% on stuttered speech recognition while only slightly degrading fluent speech performance.

Significance. If the results hold with proper verification, the work addresses a genuine gap in low-resource stuttered speech synthesis and ASR augmentation for Mandarin. Strengths include the explicit use of stuttering event labels for controllability and the non-autoregressive token approach, which could enable scalable data generation where real stuttered corpora are scarce.

major comments (3)
  1. [Results section] Results section (and abstract): The central claim of SOTA CER 4.19% for Qwen3-ASR-0.6B after augmentation provides no baseline CER on the same test set, no absolute improvement magnitude, no test-set size, and no statistical significance or confidence intervals, leaving the generalization from synthesis to real ASR improvement unverified.
  2. [Experimental setup] Experimental setup: No ablation isolating the contribution of explicit stuttering event labels versus simply increasing data volume is reported, which is load-bearing for the controllability and augmentation claims.
  3. [Methods and results] Methods and results: The assumption that <50h fine-tuning produces sufficiently diverse, controllable, and artifact-free tokens for ASR improvement lacks quantitative metrics on token diversity, reconstruction error rates, or side-effect analysis on fluent speech beyond the qualitative 'slight degradation' statement.
minor comments (1)
  1. [Abstract] Abstract and introduction: Consider adding a brief statement of the exact Mandarin stuttered test corpus and its size to allow readers to contextualize the CER result.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major comment below and will revise the manuscript to strengthen the presentation of results and experiments.

read point-by-point responses

  1. Referee: [Results section] Results section (and abstract): The central claim of SOTA CER 4.19% for Qwen3-ASR-0.6B after augmentation provides no baseline CER on the same test set, no absolute improvement magnitude, no test-set size, and no statistical significance or confidence intervals, leaving the generalization from synthesis to real ASR improvement unverified.

    Authors: We agree that these supporting details are required to fully substantiate the claim. In the revised manuscript we will add the baseline CER without augmentation on the identical test set, the absolute improvement value, the test-set size, and statistical significance or confidence intervals. revision: yes

  2. Referee: [Experimental setup] Experimental setup: No ablation isolating the contribution of explicit stuttering event labels versus simply increasing data volume is reported, which is load-bearing for the controllability and augmentation claims.

    Authors: The referee correctly notes the absence of this ablation. We will add an ablation study in the revision that directly compares the effect of explicit stuttering event labels against equivalent increases in data volume without label control. revision: yes

  3. Referee: [Methods and results] Methods and results: The assumption that <50h fine-tuning produces sufficiently diverse, controllable, and artifact-free tokens for ASR improvement lacks quantitative metrics on token diversity, reconstruction error rates, or side-effect analysis on fluent speech beyond the qualitative 'slight degradation' statement.

    Authors: We accept that additional quantitative evidence is needed. The revised version will report token diversity statistics, reconstruction error metrics, and a quantitative breakdown of effects on fluent-speech ASR performance rather than relying solely on the qualitative description. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical pipeline with external validation

full rationale

The paper's core chain is a standard synthesis pipeline (text+labels → semantic tokens via masked Transformer → prosody-aware acoustic reconstruction) trained on <50h data and evaluated via downstream ASR CER on held-out real stuttered speech. No equation or result is shown to equal its own inputs by construction, no fitted parameter is relabeled as a prediction, and no load-bearing premise rests on self-citation. All reported gains (CER 4.19%, controllability, minimal fluent-speech degradation) are presented as measured outcomes against prior methods and baselines, making the derivation self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the work relies on standard machine learning assumptions for speech token models and data augmentation; no explicit free parameters, new axioms, or invented entities are detailed beyond the framework components.

axioms (1)
  • domain assumption A non-autoregressive masked generative Transformer can effectively map text and stuttering event labels to semantic speech tokens
    This is the core mapping step described in the abstract as the basis for synthesis.

pith-pipeline@v0.9.1-grok · 5753 in / 1485 out tokens · 23660 ms · 2026-06-26T12:51:43.501898+00:00 · methodology

discussion (0)

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Reference graph

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