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arxiv: 2606.31365 · v1 · pith:QJ3KTEKJnew · submitted 2026-06-30 · 📡 eess.AS · cs.SD

Beyond Cross-Reconstruction: Probing-Based Disentanglement Evaluation for Acoustic Teleportation Codecs

Philipp Grundhuber , Emanu\"el A. P. Habets This is my paper

Pith reviewed 2026-07-01 03:43 UTC · model grok-4.3

classification 📡 eess.AS cs.SD
keywords disentanglement evaluationneural audio codecsacoustic teleportationprobing methodsroom acousticsspeaker identityvoice conversionlatent representations
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The pith

Probing reveals speaker identity stays mostly in its partition while acoustics leak into speech embeddings in neural codecs.

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

The paper introduces a probing method to evaluate how well neural audio codecs separate speech content, speaker identity, and room acoustics into distinct latent spaces. Probes regress room parameters such as reverberation time and classify speakers from each partition, with the performance difference between intended and unintended partitions serving as the disentanglement score. This approach finds that speaker identity largely remains in its assigned space but acoustic details leak into speech embeddings because of the codec's training goal. Acoustic embeddings estimate room parameters nearly as well as models trained with labels, showing that physical structure can appear without direct supervision. The method addresses the limits of cross-reconstruction tests that miss such leakage.

Core claim

The probing-based evaluation demonstrates that in an acoustic teleportation codec, speaker identity is largely confined to its intended partition while acoustic information leaks into the speech embeddings due to the training objective. Acoustic embeddings estimate room parameters within 0.02 seconds of supervised baselines without explicit supervision.

What carries the argument

Probing framework that regresses room-acoustic parameters and classifies speaker identity from latent partitions, using performance gaps between intended and unintended partitions as the disentanglement measure.

If this is right

  • Speaker embeddings primarily hold identity information with little acoustic leakage.
  • Acoustic embeddings capture meaningful room properties such as reverberation time and clarity without explicit labels.
  • Cross-reconstruction quality alone fails to detect leakage across partitions.
  • Training objectives must be adjusted to reduce acoustic information in speech embeddings for cleaner separation.

Where Pith is reading between the lines

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

  • This probing approach could test disentanglement in other latent variable models for audio or images.
  • Reducing the observed leakage might improve performance in downstream tasks like voice conversion.
  • The unsupervised emergence of room parameter estimates suggests latent spaces can implicitly learn physical acoustic models.

Load-bearing premise

The performance gap between probes on intended versus unintended partitions provides a reliable and unbiased measure of disentanglement without the probes themselves suffering from leakage or selection effects.

What would settle it

If acoustic embeddings estimate room parameters no better than chance levels or if speaker classification accuracy shows no gap between intended and unintended partitions under varied probe models, the claims on confinement and leakage would not hold.

Figures

Figures reproduced from arXiv: 2606.31365 by Emanu\"el A. P. Habets, Philipp Grundhuber.

Figure 1
Figure 1. Figure 1: Probing framework for disentanglement quantification. Left: The pre-trained AT encoder splits reverberant speech into speech (sc,r) and acoustic (hc,r) partitions. Right: Identical lightweight MLP probes trained on each partition independently predict room-acoustic parameters (regression) and speaker identity (classification). The gap ∆k (Eq. 5) between intended and unintended partition accuracy serves as … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of probe performance across four tasks: T60, C50, DRR (Pearson ρ averaged over all bands and broadband), and speaker classification top-1 accuracy. Each row corresponds to one model; each panel shows paired bars for the speech (blue) and acoustic (orange) partitions. The rightmost table reports the per-model gap ∆ with statistical significance annotations. Dashed separators indicate model family b… view at source ↗
Figure 3
Figure 3. Figure 3: Predicted versus ground-truth broadband room-acoustic parameters from the acoustic embedding of model DS Ablation Factor 4: (a) T60, (b) C50, and (c) DRR. The dashed diagonal indicates the ideal prediction. identity is effectively separated, whereas acoustic information continues to leak into the speech partition. No existing task penalizes room-acoustic information in the speech embedding. Future work cou… view at source ↗
read the original abstract

Some neural audio codecs disentangle speech into latent subspaces encoding content, speaker identity, and acoustics, enabling acoustic teleportation and voice conversion. Existing evaluations rely on cross-reconstruction quality, which cannot reliably detect leakage across partitions. We extend a probing based framework to assess disentanglement by regressing room-acoustic parameters (reverberation time, clarity, and direct-to-reverberant ratio) and classifying speaker identity, using the gap between intended and unintended partitions as the disentanglement measure. Applied to an acoustic teleportation codec, we find speaker identity is largely confined to its partition, while acoustics leak into the speech embeddings due to the training objective. Acoustic embeddings blindly estimate room parameters within 0.02 s of supervised baselines, indicating physically meaningful structure emerges without explicit supervision.

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 / 1 minor

Summary. The manuscript proposes extending a probing-based evaluation framework for disentanglement in acoustic teleportation neural audio codecs, moving beyond cross-reconstruction quality metrics. Probes regress room-acoustic parameters (reverberation time, clarity, direct-to-reverberant ratio) and classify speaker identity on latent partitions; the performance gap between intended and unintended partitions quantifies leakage. Applied to one such codec, the results indicate speaker identity is largely confined to its partition while acoustics leak into speech embeddings due to the training objective, and that acoustic embeddings can estimate room parameters within 0.02 s of supervised baselines, suggesting emergent physically meaningful structure without explicit supervision.

Significance. If the probing gaps can be shown to isolate true disentanglement, the work supplies a more direct assessment tool than cross-reconstruction for models used in voice conversion and acoustic teleportation. The reported near-parity of unsupervised acoustic embeddings with supervised room-parameter estimators is a concrete, falsifiable observation that could guide future codec design. The paper explicitly contrasts its approach with prior reliance on reconstruction quality, which is a useful contribution to evaluation methodology in the field.

major comments (2)
  1. [Abstract] Abstract: the quantitative claim that acoustic embeddings estimate room parameters 'within 0.02 s of supervised baselines' is presented without any description of probe architectures, training regimes, data splits, or statistical controls; this detail is load-bearing for the claim that physically meaningful structure emerges without explicit supervision.
  2. [Evaluation section (probing framework)] Evaluation section (probing framework): the central claim that the intended-vs-unintended partition performance gap measures leakage 'due to the training objective' rests on the assumption that probes cannot exploit data correlations between speaker and room parameters or indirect latent routes; the manuscript provides no decorrelated test sets, information-theoretic bounds, or ablation controls to rule these out, directly affecting the validity of the disentanglement conclusions.
minor comments (1)
  1. [Abstract] Abstract: the abbreviation 's' in '0.02 s gap' is ambiguous (seconds?); expand or define the metric explicitly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. Below we address each major comment point by point.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the quantitative claim that acoustic embeddings estimate room parameters 'within 0.02 s of supervised baselines' is presented without any description of probe architectures, training regimes, data splits, or statistical controls; this detail is load-bearing for the claim that physically meaningful structure emerges without explicit supervision.

    Authors: The probe architectures (linear regressors), training regimes, data splits, and controls are described in the Evaluation section. We agree the abstract presents the claim without sufficient context. We will revise the abstract to add a brief qualifier referencing the linear probes on held-out data. revision: partial

  2. Referee: [Evaluation section (probing framework)] Evaluation section (probing framework): the central claim that the intended-vs-unintended partition performance gap measures leakage 'due to the training objective' rests on the assumption that probes cannot exploit data correlations between speaker and room parameters or indirect latent routes; the manuscript provides no decorrelated test sets, information-theoretic bounds, or ablation controls to rule these out, directly affecting the validity of the disentanglement conclusions.

    Authors: We acknowledge this limitation: the manuscript uses standard dataset splits without decorrelated test sets, information-theoretic bounds, or ablations for indirect routes or speaker-room correlations. We will add a dedicated limitations paragraph qualifying the interpretation of leakage as due to the training objective and noting the assumption. The asymmetric performance gaps still provide supporting evidence, but we will tone down causal language. revision: partial

Circularity Check

0 steps flagged

No circularity; evaluation uses independent external probes

full rationale

The paper introduces a probing framework to measure disentanglement via performance gaps on intended vs. unintended latent partitions for room parameters and speaker identity. No equations, derivations, or fitted parameters are defined in terms of the target claims. The disentanglement measure is computed from separate regression/classification probes rather than reducing to codec training objectives or self-referential definitions. No self-citation chains or ansatzes are invoked as load-bearing premises. The approach is self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.1-grok · 5671 in / 1124 out tokens · 49615 ms · 2026-07-01T03:43:26.568739+00:00 · methodology

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Reference graph

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    This enables applications like Acoustic Teleporta- tion (AT), dereverberation, and voice conversion [1]

    Introduction Certain Neural Audio Codecs (NACs) partition the latent space in order to disentangle distinct speech attributes, including lin- guistic content, speaker identity, prosody, and acoustic envi- ronment. This enables applications like Acoustic Teleporta- tion (AT), dereverberation, and voice conversion [1]. Recent codecs realize this idea throug...

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    Beyond Cross-Reconstruction: Probing-Based Disentanglement Evaluation for Acoustic Teleportation Codecs

    Problem Formulation In acoustic teleportation, a reverberant speech signal is modeled asx c,r =s c ∗h r, wheres c is anechoic speech with contentc, hr is the room impulse response (RIR) of roomr, and∗denotes convolution. The encoder splits the input into two partitions (Fig. 1): {sc,r,h c,r}=Enc(x c,r),(1) wheres c,r ∈R Ts×64 captures speech content andh ...

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    Proposed Probing Method 3.1. Probing Framework We estimate∆ acc and∆ ρ by treating the pre-trained encoder of the AT models from [7] as a fixed feature extractor and train- ing lightweight probes on each embedding partition indepen- dently. The encoder is an EnCodec-based [20] NAC operating at 16 kHz with a hop length of 320 samples. It produces 128- dime...

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    Disentanglement Disentanglement was evaluated by comparing the gaps∆ acc and∆ ρ

    Evaluation 4.1. Disentanglement Disentanglement was evaluated by comparing the gaps∆ acc and∆ ρ. Significance of∆ ρ was assessed via the Steiger test [23] for dependent correlations, which accounts for the shared test set and ground-truth labels;∆ acc was tested with a two-proportionz-test. Significance thresholds followp≤0.05 (*),p≤0.01(**),p≤0.001(***),...

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    This di- rectly penalizes speaker leakage into the acoustic embedding

    Discussion The asymmetry between speech and acoustic embedding leak- age can be attributed back to the AT training objective: Em- bedding swaps across speakers force the decoder to recover speaker identity exclusively from the speech partition. This di- rectly penalizes speaker leakage into the acoustic embedding. No analogous task minimizes room-acoustic...

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    Conclusion We introduced a probing-based framework that quantifies dis- entanglement in NACs by training lightweight regressors and classifiers on fixed embedding partitions, thereby directly mea- suring the information content per partition and per factor. Ap- plying this framework to an AT codec reveals an asymmetric disentanglement structure: speaker i...

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