Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models

Artyom Stitsyuk; Gunu Jho; Inchul Hwang; Jaesik Choi; Kyowoon Lee

REVIEW 5 major objections 5 minor 42 references

Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models

T0 review · 5 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Editing a pretrained TTS model's inner activations at inference time can scale pitch, duration, and energy and fix mispronunciations, without retraining.

desk verdict A genuinely new combination for post-hoc TTS control, with honest but incomplete evidence: the causal link between probe gradients and decoder behavior is not tested, and the prosody metrics share their label pipeline. read the letter →

arxiv 2506.00832 v1 pith:YTD2WVRW submitted 2025-06-01 cs.SD cs.AIeess.AS

Kyowoon Lee , Artyom Stitsyuk , Gunu Jho , Inchul Hwang , Jaesik Choi This is my paper

classification cs.SDcs.AIeess.AS

keywords text-to-speechprosodycontrolmispronunciationcorrectionactivationeditingcounterfactualexplanationsTacotron2inference-timerepresentationmanipulation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper claims that a pretrained text-to-speech encoder stores enough information about pitch, duration, energy, and pronunciation that a simple classifier can read those properties out of its activations, and that pushing the activations along the classifier's gradient changes the synthesized audio accordingly. The method, Counterfactual Activation Editing (CAE), performs this push inside a latent space learned by a beta-VAE, with a prototype loss that keeps edits near familiar pronunciation patterns. If true, a user could take an already trained Tacotron 2 and, at inference time, slow down a word, raise the pitch of a phoneme, or correct a mispronunciation without retraining or pronunciation dictionaries. The paper reports that these edits preserve overall synthesis quality and, for mispronunciation correction, improve word error rate, phone error rate, and semantic similarity.

What carries the argument

The load-bearing object is the linear probe $f$ mapping an encoder activation $x_i$ to an acoustic property, combined with the counterfactual update $x_i' = x_i + \eta \, \partial f_c / \partial x_i$. To keep the edited activation on the natural speech manifold, the update is performed in the 16-dimensional latent space of a $\beta$-VAE after mapping the activation through the VAE encoder, and a prototype loss from a VQ-VAE codebook pulls the edited latent back toward the nearest discrete prototype so that edits do not cross into subregions that change the spoken content. The claim that such edits affect the final audio rests on the assumption that the probe directions are causally aligned with the decoder's generation pathway.

What would settle it

For a fixed utterance, compare the CAE edit with a random perturbation of the same magnitude in activation space; if the random perturbation moves measured pitch, duration, or pronunciation by as much as the gradient edit does, the claimed causal alignment between the probe and the speech decoder is unsupported.

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Extended reading notes

Core claim

The central discovery is that the final encoder layer of Tacotron 2, without any specialized training for control, encodes acoustic properties in a directionally usable form. Linear regressors trained on those activations predict duration, pitch, and energy well enough that gradient ascent toward a target value produces audible, roughly proportional changes in the synthesized speech, and a classifier over HuBERT-derived semantic tokens does the same for pronunciation. A naive edit in the raw activation space drifts off the data manifold, so the paper edits in the bottleneck of a beta-VAE and anchors the edit with a VQ-VAE prototype; this recovers predictable scaling of prosody and reduces mispronunciations. The authors present this as evidence that post-hoc decomposability, not a specially trained control module, is enough to give an encoder-decoder TTS model editable outputs.

Load-bearing premise

The method assumes that the direction a linear probe finds in activation space is causally aligned with what the decoder turns into audio, so that gradient ascent on the probe actually raises pitch, slows speech, or fixes pronunciation rather than just fooling the probe.

Editorial extensions

If this is right

Any pretrained encoder-decoder TTS whose activations linearly encode these acoustic properties could be steered by the same recipe, without retraining.
Prosody control becomes a per-phoneme or per-word operation, so users can scale duration or pitch for a single word within an utterance.
Mispronunciation correction no longer requires a grapheme-to-phoneme dictionary, which matters for low-resource languages with sparse pronunciation resources.
A single correction query, in the form of a speech sample of the intended pronunciation, is enough to define the target semantic token for the edit.
The edit procedure can be applied after synthesis at inference, enabling iterative refinement of the output before it is delivered to the listener.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the linear-probe alignment generalizes beyond Tacotron 2, this turns any controllable probe into a control handle, but the paper only demonstrates causal alignment for one architecture, so cross-model transfer is untested.
The feature entanglement the paper acknowledges (duration edits shift pitch) suggests a neuron-level variant that edits only the highest-weight dimensions could yield more orthogonal control; this is an extension the authors flag as future work.
One testable extension is to use the same activation-editing recipe on non-autoregressive TTS models, where encoder activations may have different geometry and the manifold-preserving step may matter more.
If the probe outputs are calibrated as a monotone control signal, CAE could be wrapped into a closed loop that measures actual prosody with an external pitch tracker and iterates until the desired value is reached; the paper does not report such a loop.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The useful new thing here is the combination: linear probes on encoder activations plus counterfactual gradient ascent in a β-VAE latent space, anchored by a VQ-VAE prototype loss, for three prosody dims and pronunciation, all after training. Components are borrowed, but nobody has put them together this way for TTS, and the paper shows it works on Tacotron 2. The Whisper-based evaluation for mispronunciation is independent and the reported PER/WER gains are meaningful, though 78 words is a small sample and there is no comparison against SAC or dictionary-based methods. The prosody curves in Figure 2 are monotonic and the spectrograms show real changes, so the central mechanism is plausible. The paper also deserves credit for openly showing feature entanglement in Figure 5 and discussing it.

That said, the load-bearing assumption—that the probe's gradient is a causal handle on the decoder—is not checked. No shuffled-label control, no random-direction control, no second architecture. The manifold-preserving machinery keeps edits near the training distribution, but that does not establish that the edits cause the intended acoustic change rather than just statistically plausible drift. The shared label-extraction pipeline for prosody (same alignments and pitch/energy compute) makes part of the measured change potentially a measurement-side effect. The absence of error bars and significance tests is a real gap, especially for comparing variants. No code or data artifacts are provided, so reproducibility rests on the demo page and the description.

These are mainly completeness problems, not signs the method is fake. The central idea is well motivated and the execution is competent. The model-agnostic claim is overreached from one architecture, but that is fixable. I would send this to peer review: it is a legitimate empirical contribution that a good reviewer could push into a solid conference paper. I would not cite it as a proven technique yet, but I would cite it as evidence that activation editing can steer prosody in a pretrained attention-based TTS model, provided the missing controls get added in revision.

For reading group: worth a slot, especially if someone wants to discuss causal claims versus correlational evidence in representation editing.

Referee Report

5 major / 5 minor

Summary. The paper introduces Counterfactual Activation Editing (CAE), a post-hoc method for controlling prosody and correcting mispronunciations in a pre-trained TTS model. The authors first train linear classifiers/regressors on intermediate encoder activations of Tacotron 2 to predict prosodic features (duration, pitch, energy) and semantic tokens derived from HuBERT. They then perform gradient ascent on the probe output, either directly in activation space or in the latent space of a beta-VAE with a prototype loss, to shift activations toward a target property. Experiments on LJSpeech demonstrate monotonic changes in measured prosodic features, improved phone-error-rate under large prosody edits, and reduced WER/PER for mispronounced words, together with a CMOS preference for corrected speech. The paper claims the method is model-agnostic and requires no retraining of the TTS model.

Significance. If the central claim holds, the paper provides a useful inference-time steering mechanism for encoder-decoder TTS systems, complementing existing retraining-based prosody controls and dictionary-based pronunciation correction. The work has several strengths: the idea of editing pretrained TTS representations is clearly presented; the mispronunciation evaluation uses Whisper, an independent ASR metric; and the demo page and audio examples are valuable. However, the significance is currently limited by the lack of causal validation, the use of a single TTS architecture, and the absence of statistical uncertainty estimates, all of which are needed to support the stated model-agnostic, post-hoc controllability claim.

major comments (5)

[Section 3.1, Section 5.2, Figs. 2–3] The prosody evaluation is coupled to the label-generation pipeline used to train the probe. Section 3.1 describes extracting durations via a CTC/ASR system with monotonic alignment search, and then computing average pitch and energy from those alignments; the 'change ratio' in Figure 2 is measured with the same kinds of features. If the edit moves the probe's input along its gradient, the same pipeline will report a change even when an independent acoustic measurement would not. Please add an independent measurement (e.g., a different forced aligner, manual alignments, or an external pitch tracker) or demonstrate agreement on a held-out subset.
[Section 3.2, Eqs. (1) and (4), Fig. 2, Table 1] The central causal claim is that the probe-gradient direction is a causal handle on the decoder. The experiments never include a negative control: random directions with matched norm, a probe trained on shuffled labels, or edits that only increase the probe output without target-specific direction. Without such a control, the observed duration/pitch/energy shifts could be a generic effect of moving activations away from their original values rather than evidence of the specific encoded property. Please add at least one negative-control condition to support the causal interpretation.
[Section 4.1, Section 5, Abstract] The method is repeatedly described as model-agnostic, but all experiments use a single Tacotron 2 instantiation with WaveGlow. A second encoder-decoder TTS model (e.g., Transformer TTS or a non-autoregressive model) is needed to support the generality claim; the current evidence supports only 'works for one model'.
[Section 5.3, Table 1] The mispronunciation experiment uses only 78 target words and reports aggregate means over them without confidence intervals or significance tests. It also does not compare against the SAC baseline [21] discussed in Section 2.2, despite the method being positioned as an alternative. Please include the SAC comparison (or a rationale for its absence), report uncertainty, and consider a per-word breakdown since 78 samples is small.
[Section 3.2.2, Section 4.3] Several hyperparameters needed to reproduce the method are missing: the VQ-VAE codebook size K and dimension D, the prototype loss weight alpha in Eq. (7), the gradient step size eta in Eqs. (1) and (4), and the stopping threshold for the classifier output. Please report these values, or provide exact code.

minor comments (5)

[Figure 1] The caption says 'Loss' but it is unclear whether lower is better; please clarify that the normalized classifier prediction loss is shown and specify which classifier and training details are used.
[Eq. (2)] The neighborhood radius r is said to depend on the step size eta, but no formula or value is given; please state the relationship or remove the claim.
[Section 3.1] The HuBERT k-means clustering uses 100 clusters, but it is not stated what data the k-means is fit on or how many frames are used; please clarify.
[Table 2] The CMOS convention is unclear: 'After Correction 0.000' and 'Before Correction -0.764' suggests the corrected speech is the reference; please state this explicitly.
[Section 5.2] The truncation trick [32] is compared against, but the description is vague; please give a one-sentence definition of how it is applied to encoder activations.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the prosody and mispronunciation controls are validated through the frozen TTS synthesis path and independent measurement, with no fitted parameter renamed as a prediction.

full rationale

The paper's derivation chain is not circular. The linear probes (regressor/classifier) are fit to acoustic/semantic labels extracted from original LJSpeech audio, but the controlled outputs are generated by the frozen Tacotron 2 encoder-decoder plus WaveGlow vocoder and then measured from the synthesized waveform or via Whisper ASR. For prosody, the evaluation reports the ratio of the modified acoustic feature measured after synthesis to the original value, not the probe output f(x') itself; nothing in Eqs. (1) or (4) equates the probe output to the measured acoustic feature, so the link is an empirical causal claim about the decoder, not a definitional identity. For mispronunciation, the evaluation uses independent Whisper-based WER/PER and text-embedding similarity, which does not share the training-label pipeline. The paper contains no load-bearing self-citations: references to HuBERT, beta-VAE, VQ-VAE, Whisper, and prior TTS work are external. The absence of a shuffled-label or random-direction negative control and the reliance on a single architecture are correctness/generality limitations, not circularity. No step can be exhibited where an equation reduces to its own input or where a fitted parameter is renamed as a prediction.

Assumptions & free parameters 8 free parameters · 6 assumptions · 0 invented entities

The method introduces no new physical entities; the 'prototype' is a method component derived from a VQ-VAE codebook, not an independently evidenced entity. The central claim rests on several unproven modeling assumptions: probe directions align with the decoder's causal control, beta-VAE and VQ-VAE latent geometry preserve content, and HuBERT tokens are valid pronunciation targets. Many hyperparameters such as beta, alpha, eta, threshold, and codebook sizes are chosen by hand or unreported, with no sensitivity analysis.

free parameters (8)

beta-VAE weight beta = 0.04
Section 4.3 sets beta=0.04 by hand for manifold training; the method's behavior depends on this choice and no sensitivity analysis is given.
beta-VAE latent dimension = 16
Section 4.3 selects a 16-dimensional latent space; no ablation varying dimensionality is reported.
Prototype loss weight alpha = not reported
Eq. (7) introduces Lproto = alpha * ||zi - proto(zi)||^2, but alpha is never specified in the paper.
Gradient step size eta = not reported
Eqs. (1) and (4) depend on eta, but its value and schedule are omitted.
Classifier stopping threshold = not reported
Section 3.2 says the procedure continues until the classifier output for the target class exceeds a chosen threshold, but the threshold is not given.
Elastic net regularization lambda_1 and lambda_2 = 0.001 each
Section 4.3 sets these hand-chosen hyperparameters for the regression probes that define the edit directions.
HuBERT k-means cluster count = 100
Section 3.1 discretizes semantic representations into 100 clusters; this granularity directly affects the pronunciation targets.
VQ-VAE codebook size K and dimension D = not reported
Eqs. (5) and (6) rely on a learned codebook with K embeddings of dimension D, but K and D are not specified.

assumptions (6)

domain assumption HuBERT layer-6 semantic tokens represent pronunciation-relevant information at the word or phoneme level.
Used to define pronunciation labels in Section 3.1; relies on references [21,23,24] and is not independently validated in this paper.
domain assumption Linear classifiers are sufficient to probe TTS encoder representations.
Section 3.1 cites [25,26] to justify linear probing; nonlinear probing performance could differ and the paper does not test it.
ad hoc to paper The beta-VAE latent space approximates the encoder activation manifold, so gradient edits in the latent space stay on the data manifold.
Section 3.2.1 assumes the information bottleneck learns manifold geometry; no direct manifold-adherence validation is provided.
ad hoc to paper VQ-VAE prototypes anchor edits to a content-preserving subregion of the latent space.
Section 3.2.2 introduces the prototype loss to avoid content changes; evidence is only indirect through PER comparisons and no proof or full ablation is given.
domain assumption Editing encoder activations causes the decoder and vocoder to generate speech with the intended acoustic change.
The entire method depends on the causal link between probe directions and generation; Section 5.2 tests this empirically on one model, but the assumption is not derived.
domain assumption ASR alignments and acoustic extraction from LJSpeech provide accurate duration, pitch, and energy labels.
Section 3.1 uses CTC and monotonic alignment search; errors in alignment would propagate into probe targets and all prosody measurements.

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Cite this review

Pith. "Pith review of Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models." pith.science (2026). https://pith.science/paper/YTD2WVRW

@misc{pith2026250600832,
  author       = {Pith},
  title        = {Pith review of: Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YTD2WVRW}},
  note         = {Machine review of arXiv:2506.00832}
}

read the original abstract

Recent advances in Text-to-Speech (TTS) have significantly improved speech naturalness, increasing the demand for precise prosody control and mispronunciation correction. Existing approaches for prosody manipulation often depend on specialized modules or additional training, limiting their capacity for post-hoc adjustments. Similarly, traditional mispronunciation correction relies on grapheme-to-phoneme dictionaries, making it less practical in low-resource settings. We introduce Counterfactual Activation Editing, a model-agnostic method that manipulates internal representations in a pre-trained TTS model to achieve post-hoc control of prosody and pronunciation. Experimental results show that our method effectively adjusts prosodic features and corrects mispronunciations while preserving synthesis quality. This opens the door to inference-time refinement of TTS outputs without retraining, bridging the gap between pre-trained TTS models and editable speech synthesis.

Figures

Figures reproduced from arXiv: 2506.00832 by the authors.

**Figure 1.** Acoustic Correlation Analysis. (a) Classifier prediction losses across Tacotron 2 encoder layers, normalized with the highest value as one. (b) Effect of training only the most or least important neurons on classifier performance. fully internalizing them is difficult, leading to inevitable pronunciation errors in synthesized speech. To mitigate mispronunciations, unit selection concatenates recorded speech fragme… view at source ↗

**Figure 2.** Ratio of change in prosodic features. Values above 1 indicate an increase, while values below 1 indicate a decrease. where g is the decoder mapping from the latent space Z back to the original activation space X. By constraining edits within Z, the resulting activations lie closer to the manifold M, yielding counterfactual changes that are semantically meaningful. 3.2.2. Prototype Loss Even with manifold preserving … view at source ↗

**Figure 5.** Phoneme pitch density over the LJSpeech dataset after Manifold Preserving CAE duration control. To evaluate pronunciation correction, we synthesized 78 speech samples, each with a target word in the carrier sentence “How is ... pronounced?”. These target words were selected from prior work [21] as being outside the LJSpeech training set or challenging for grapheme-input TTS models. Mispronunciation correction, usin… view at source ↗

Figures from the paper (1 more)

**Figure 4.** Figure 4: Spectrograms of the synthesized speech using Manifold Preserving CAE for prosody control. The input text used for synthesis is “In planning its data processing techniques.” 5.2. Prosody Control We investigate Counterfactual Activation Editing (CAE) for controlling pro…

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Works this paper leans on

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Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis

D. S. R. Mohan, V . Hu, T. H. Teh, A. Torresquintero, C. G. Wallis, M. Staib, L. Foglianti, J. Gao, and S. King, “Ctrl-p: Temporal control of prosodic variation for speech synthesis,”arXiv preprint arXiv:2106.08352, 2021

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What would the internal representation look like if the model aimed for a higher pitch rather than the current, lower one?

Introduction Advancements in Text-to-Speech (TTS) models [1, 2, 3, 4, 5] and neural vocoders [6, 7, 8, 9] have made synthetic voices nearly indistinguishable from human speech. Consequently, more attention has been attracted by refining the expressiveness of synthetic voices, particularly through the accurate manipula- tion of prosodic features and pronun...

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Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models

Related Work 2.1. TTS Prosody Control Text can be spoken in various ways due to semantic nuances, speaking styles, or inherent variability. Traditional approaches, such as unit-selection, capture this variability through speech databases [17]. In contrast, recent studies model prosodic vari- ation by predicting key prosodic features such as pitch, dura- t...

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What would the internal representation look like if the synthesized speech had a higher pitch rather than a lower pitch?

Proposed Method We first analyze intermediate representations produced by the encoder of an end-to-end TTS model, applicable to any encoder-decoder architectures. Consider a sequence of input featurest={t 1, . . . ,tn}, and letMbe a neural network model that transformstinto a se- ries of internal representationst M − →x={x1, . . . ,xn}. In this context,Mr...

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Experimental Setup 4.1. TTS Model We experimented with Tacotron 2 [2] encoder-decoder archi- tecture, a model with about 28 million parameters, including an encoder with three convolutional layers followed by an LSTM layer. Although Tacotron 2 generates highly natural speech, it lacks explicit mechanisms to capture diverse prosodic contours without additi...

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In planning its data processing techniques

Results Our experiments study the following questions: (1) Are acous- tic features encoded by the Tacotron 2 encoder? (2) Can counterfactual activation editing(CAE) effectively manipulate prosody? (3) Can CAE further correct mispronunciations? 5.1. Acoustic Correlation Analysis To assess the capacity of Tacotron 2 encoder layers to encap- sulate meaningfu...

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Conclusions and Discussion In this work, we introduced a novel method for prosody con- trol and pronunciation correction in TTS models using Counter- factual Activation Editing, enabling post-hoc adjustments with- out retraining. Our experiments demonstrated that intermedi- ate representations within TTS models, specifically Tacotron 2, contain rich infor...

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2022-0-00984, Devel- opment of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation; No

Acknowledgements This work was supported by Samsung Electronics MX Divi- sion and by Institute of Information & communications Tech- nology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-00075, Artificial Intelligence Graduate School Program (KAIST); No. 2022-0-00984, Devel- opment of Artificial Intelligence Technology...

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [21]

Ctrl-P: Temporal Control of Prosodic Variation for Speech Synthesis

D. S. R. Mohan, V . Hu, T. H. Teh, A. Torresquintero, C. G. Wallis, M. Staib, L. Foglianti, J. Gao, and S. King, “Ctrl-p: Temporal control of prosodic variation for speech synthesis,”arXiv preprint arXiv:2106.08352, 2021

work page Pith review arXiv 2021

[2] [1]

What would the internal representation look like if the model aimed for a higher pitch rather than the current, lower one?

Introduction Advancements in Text-to-Speech (TTS) models [1, 2, 3, 4, 5] and neural vocoders [6, 7, 8, 9] have made synthetic voices nearly indistinguishable from human speech. Consequently, more attention has been attracted by refining the expressiveness of synthetic voices, particularly through the accurate manipula- tion of prosodic features and pronun...

work page

[3] [2]

Counterfactual Activation Editing for Post-hoc Prosody and Mispronunciation Correction in TTS Models

Related Work 2.1. TTS Prosody Control Text can be spoken in various ways due to semantic nuances, speaking styles, or inherent variability. Traditional approaches, such as unit-selection, capture this variability through speech databases [17]. In contrast, recent studies model prosodic vari- ation by predicting key prosodic features such as pitch, dura- t...

work page Pith review arXiv 2025

[4] [3]

What would the internal representation look like if the synthesized speech had a higher pitch rather than a lower pitch?

Proposed Method We first analyze intermediate representations produced by the encoder of an end-to-end TTS model, applicable to any encoder-decoder architectures. Consider a sequence of input featurest={t 1, . . . ,tn}, and letMbe a neural network model that transformstinto a se- ries of internal representationst M − →x={x1, . . . ,xn}. In this context,Mr...

work page

[5] [4]

Experimental Setup 4.1. TTS Model We experimented with Tacotron 2 [2] encoder-decoder archi- tecture, a model with about 28 million parameters, including an encoder with three convolutional layers followed by an LSTM layer. Although Tacotron 2 generates highly natural speech, it lacks explicit mechanisms to capture diverse prosodic contours without additi...

work page

[6] [5]

In planning its data processing techniques

Results Our experiments study the following questions: (1) Are acous- tic features encoded by the Tacotron 2 encoder? (2) Can counterfactual activation editing(CAE) effectively manipulate prosody? (3) Can CAE further correct mispronunciations? 5.1. Acoustic Correlation Analysis To assess the capacity of Tacotron 2 encoder layers to encap- sulate meaningfu...

work page

[7] [6]

Conclusions and Discussion In this work, we introduced a novel method for prosody con- trol and pronunciation correction in TTS models using Counter- factual Activation Editing, enabling post-hoc adjustments with- out retraining. Our experiments demonstrated that intermedi- ate representations within TTS models, specifically Tacotron 2, contain rich infor...

work page

[8] [7]

2022-0-00984, Devel- opment of Artificial Intelligence Technology for Personalized Plug-and-Play Explanation and Verification of Explanation; No

Acknowledgements This work was supported by Samsung Electronics MX Divi- sion and by Institute of Information & communications Tech- nology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No.2019-0-00075, Artificial Intelligence Graduate School Program (KAIST); No. 2022-0-00984, Devel- opment of Artificial Intelligence Technology...

work page 2019