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arxiv: 2604.07467 · v1 · submitted 2026-04-08 · 💻 cs.CL · cs.LG

Lexical Tone is Hard to Quantize: Probing Discrete Speech Units in Mandarin and Yor\`ub\'a

Opeyemi Osakuade , Simon King This is my paper

Pith reviewed 2026-05-10 17:13 UTC · model grok-4.3

classification 💻 cs.CL cs.LG
keywords lexical tonediscrete speech unitsquantizationself-supervised learningMandarinYorubaprosodyspeech representation
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The pith

Quantization of self-supervised speech representations prioritizes phonetic structure over lexical tone.

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

The paper tests whether discrete speech units produced by quantizing SSL model latents preserve the lexical tone information that the continuous representations already contain. Experiments on Mandarin and Yoruba show that tone is encoded reliably in the latents but becomes much less recoverable once the representations are discretized, and that this pattern holds for multiple quantization techniques. A simple two-stage clustering procedure that first captures phonetics and then clusters the residuals improves tone recovery. The authors conclude that standard quantization strategies are inherently limited for suprasegmental features and call for tone-aware or prosody-aware alternatives.

Core claim

Discrete speech units obtained by quantizing SSL latent representations encode lexical tone less reliably than the original continuous latents because quantization favors segmental phonetic structure; this limitation persists across different quantization methods, as demonstrated by probing experiments on tone-labeled Mandarin and Yoruba speech data.

What carries the argument

Probing classifiers that measure tone classification accuracy from discrete units versus continuous latents, together with a residual K-means procedure that clusters phonetics first and then the residual representation to retain tone.

If this is right

  • Standard DSUs are likely suboptimal for downstream tasks that depend on prosody or tone, such as text-to-speech synthesis and multimodal dialogue in tonal languages.
  • SSL latent representations contain usable tone information that is systematically discarded by current discretization pipelines.
  • A residual clustering step after initial phonetic quantization can recover some of the lost tone information without retraining the underlying SSL model.
  • New quantization techniques explicitly designed to preserve suprasegmental features are required for high-quality speech representations in tone languages.

Where Pith is reading between the lines

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

  • The same quantization bias probably affects other suprasegmental cues such as intonation, stress, and rhythm in non-tonal languages.
  • Multilingual or low-resource speech systems may inherit systematic disadvantages for tone languages unless quantization is redesigned.
  • Joint text-speech models that rely on DSUs could see improved performance on tonal languages if tone-preserving discretization becomes standard.

Load-bearing premise

The chosen probing classifiers and tone-labeled datasets isolate lexical tone encoding without confounding effects from speaker, context, or dataset-specific artifacts.

What would settle it

A quantization method that produces discrete units from which tone can be classified at least as accurately as from the original continuous latents, while still preserving phonetic discriminability, would falsify the central claim.

Figures

Figures reproduced from arXiv: 2604.07467 by Opeyemi Osakuade, Simon King.

Figure 1
Figure 1. Figure 1: Weighted F1 scores for Mandarin and Yorub` a phone and tone classification using K-Means codebooks of varying sizes. ´ Solid lines represent no pooling (phone segment-level), while dashed lines represent pooled (phone segment-level) features. Dotted lines denote the corresponding HuBERT latent baselines. The x-axis is logarithmic to highlight performance gains across orders of magnitude in codebook size. 2… view at source ↗
Figure 2
Figure 2. Figure 2: L1 refers to first-pass K-means on the mean-pooled phone segment at different K; L2 represents clustering on resid￾uals. While L1 performs well for vowels, L2 boosts tone clas￾sification, getting closer to the original (unquantised) latents. Dotted lines indicate latent baselines. results in a set of K centroids (i.e., vectors in latent space). Each frame in the dataset is then replaced with the closest ce… view at source ↗
read the original abstract

Discrete speech units (DSUs) are derived by quantising representations from models trained using self-supervised learning (SSL). They are a popular representation for a wide variety of spoken language tasks, including those where prosody matters. DSUs are especially convenient for tasks where text and speech are jointly modelled, such as text-to-speech and multimodal dialogue systems. But we have found that DSUs encode suprasegmental information less reliably than segmental structure, which we demonstrate in this work using lexical tone, though this limitation likely extends to other suprasegmental features such as prosody. Our investigations using the tone languages Mandarin and Yor\`ub\'a show that the SSL latent representations themselves do encode tone, yet DSUs obtained using quantisation tend to prioritise phonetic structure, which makes lexical tone less reliably encoded. This remains true for a variety of quantisation methods, not only the most common, K-means. We conclude that current DSU quantisation strategies have limitations for suprasegmental features, which suggests a need for new, tone-aware (or prosody-aware) techniques in speech representation learning. We point towards a potential form of the solution by performing K-means clustering once to encode phonetic information, then again on the residual representation, which better encodes lexical tone.

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

2 major / 2 minor

Summary. The paper claims that SSL latent representations encode lexical tone in Mandarin and Yorùbá, but DSUs from quantization (K-means and others) prioritize phonetic structure and encode tone less reliably; this holds across quantizers, and a two-stage residual clustering approach is proposed to improve tone capture while retaining phonetic information.

Significance. If the probing results are robust, the work identifies a practically important limitation of current DSU methods for suprasegmental features in tone languages, with direct relevance to TTS, multimodal dialogue, and prosody modeling. The multi-language, multi-quantizer design and the concrete residual-clustering suggestion are strengths that could guide follow-on representation learning.

major comments (2)
  1. [Methods / Probing setup] Methods and experimental setup: the central claim that quantization causes a drop in tone encoding (relative to continuous latents) depends on the probing classifiers and tone-labeled datasets isolating lexical tone rather than correlated phonetic, speaker, or contextual signals. No details are provided on dataset sizes, speaker balancing, controls for tone-vowel co-occurrence, or utterance-level context, nor are error bars or statistical tests reported; this makes it impossible to verify whether the observed drop is specific to tone or to proxy features.
  2. [Results / Discussion] Results interpretation: the abstract states that SSL latents encode tone yet DSUs do not, but without ablation studies removing phonetic content or speaker identity from the probes, the comparison between continuous and discrete representations risks confounding the effect of quantization with loss of non-tone information.
minor comments (2)
  1. [Abstract and throughout] Notation: the language name appears inconsistently as 'Yor`ub'a' and 'Yorùbá'; standardize throughout.
  2. [Figures] Figures: ensure all plots of classification accuracy include error bars, legend entries for each quantizer, and explicit comparison to the continuous baseline.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the work's significance. We address the major comments point by point below.

read point-by-point responses

  1. Referee: [Methods / Probing setup] Methods and experimental setup: the central claim that quantization causes a drop in tone encoding (relative to continuous latents) depends on the probing classifiers and tone-labeled datasets isolating lexical tone rather than correlated phonetic, speaker, or contextual signals. No details are provided on dataset sizes, speaker balancing, controls for tone-vowel co-occurrence, or utterance-level context, nor are error bars or statistical tests reported; this makes it impossible to verify whether the observed drop is specific to tone or to proxy features.

    Authors: We agree that additional details on the datasets and probing setup are required to strengthen the claims and allow verification that the tone-encoding drop is attributable to quantization. In the revised manuscript we will expand the Methods section with dataset sizes, speaker balancing, any implemented controls for tone-vowel co-occurrence and utterance-level context, plus error bars and statistical tests on the probing accuracies. revision: yes

  2. Referee: [Results / Discussion] Results interpretation: the abstract states that SSL latents encode tone yet DSUs do not, but without ablation studies removing phonetic content or speaker identity from the probes, the comparison between continuous and discrete representations risks confounding the effect of quantization with loss of non-tone information.

    Authors: We acknowledge the risk of confounding. The current design applies identical tone probes to both continuous and discrete representations, so any performance gap is due to the quantization step itself. To further isolate tone, we will add ablation experiments (or expanded discussion of existing controls) in the revision; if full ablations are not feasible we will explicitly note the limitation. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical probing study with independent results

full rationale

This is an empirical investigation that applies standard SSL models, multiple quantization methods (including but not limited to k-means), and probing classifiers to externally labeled Mandarin and Yorùbá tone datasets. No derivations, equations, or first-principles claims appear; the central observation (DSUs encode tone less reliably than continuous latents) is measured directly from classification accuracies on held-out data. The residual-clustering suggestion is presented as a forward-looking proposal, not as a redefinition or fit that tautologically reproduces the input observations. No self-citations are invoked to justify uniqueness or forbid alternatives, and all measurements rest on independent, publicly available resources rather than parameters fitted to the target quantity itself.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that standard SSL models and tone-annotated corpora provide an unbiased view of tone encoding; no new entities or fitted constants are introduced beyond routine clustering hyperparameters.

free parameters (1)
  • number of clusters K
    Chosen for each quantizer; affects how phonetic vs tonal information is partitioned.
axioms (1)
  • domain assumption SSL latent representations encode both segmental and suprasegmental information
    Invoked to interpret why tone is present before but not after quantization.

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Reference graph

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    Introduction Self-supervised learning (SSL) has become a key component of many speech processing systems, providing rich latent rep- resentations that encode phonetic, lexical, and prosodic infor- mation [1, 2, 3]. To use these continuous representations in downstream tasks, it is often necessary to discretise them into Discrete Speech Units (DSUs). DSUs ...

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    Lexical Tone is Hard to Quantize: Probing Discrete Speech Units in Mandarin and Yor\`ub\'a

    Method Our method involves extracting SSL representations from pre- trained foundation models, quantising them using various meth- ods, then probing for both phonetic and tonal information. arXiv:2604.07467v1 [cs.CL] 8 Apr 2026 Figure 1:Weighted F1 scores for Mandarin and Yor `ub´a phone and tone classification using K-Means codebooks of varying sizes. So...

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