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arxiv: 2309.09346 · v1 · submitted 2023-09-17 · 💻 cs.AI · cs.RO

Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents

Carson Yu Liu , Gelareh Mohammadi , Yang Song , Wafa Johal This is my paper

Pith reviewed 2026-05-24 06:13 UTC · model grok-4.3

classification 💻 cs.AI cs.RO
keywords gesture generationconditional GANco-speech gesturesspeech to gestureembodied agentsjoint anglesnonverbal behaviorrobot interaction
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The pith

A conditional GAN generates sequences of joint angles from speech text and audio for embodied agents.

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

The paper aims to establish that a conditional GAN can produce co-speech gesture sequences by learning mappings from both the meaning and sound properties of speech. This capability would let robots and virtual agents add natural nonverbal signals to their interactions without manual keyframing. The network is trained on paired speech and motion data from one speaker, then tested with both numerical metrics and human judgments. If the mapping holds, agents gain a practical way to synchronize body movement with spoken content in real time. The work focuses on showing this pipeline works end-to-end for the given dataset.

Core claim

Based on a conditional Generative Adversarial Network, the neural network model learns the relationships between the co-speech gestures and both semantic and acoustic features from the speech input, enabling generation of joint angle sequences from speech text and audio utterances, as shown effective by objective and subjective evaluations on a public dataset.

What carries the argument

conditional Generative Adversarial Network (GAN) that maps speech text and audio to sequences of joint angles

If this is right

  • Embodied agents can generate co-speech gestures automatically from ordinary speech input.
  • Both semantic content and acoustic properties of speech are used to shape the output movements.
  • The same framework applies to virtual agents and physical robots.
  • Objective measures and human subjective ratings both support the quality of the generated sequences.

Where Pith is reading between the lines

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

  • Retraining on data from additional speakers would likely be needed before the system could match individual gesture styles.
  • Coupling the model with live text-to-speech output could support fully autonomous spoken interaction.
  • Adaptation layers may be required when the target agent has different joint limits or body proportions.
  • The approach could be extended to generate gestures that respond to dialogue context beyond the current utterance.

Load-bearing premise

A dataset captured from one male native English speaker contains enough variety to train a model that generalizes to other speakers and different embodied agents.

What would settle it

Apply the trained model to speech recordings from a different speaker or language and observe whether human raters judge the resulting gestures as significantly less natural than those produced on the original dataset.

Figures

Figures reproduced from arXiv: 2309.09346 by Carson Yu Liu, Gelareh Mohammadi, Wafa Johal, Yang Song.

Figure 2
Figure 2. Figure 2: Gesture Generator First, we concatenate the text embedding, MFCCs and random noise as a long vector, then send them through to the two-layer bi-direction GRU (Gated recurrent unit) with 0.2 dropouts. Next, the vector passes through the following linear layer with the TanH activation function to reduce the dimensionality of the feature. In order to ensure the continuity of generated gestures, we used the fe… view at source ↗
Figure 3
Figure 3. Figure 3: Discriminator L W GAN G = − 1 N Xn i=1 D(sa,st, ˆgi ) (5) LD = 1 N Xn i=1 D(sa,st, ˆgi ) − 1 N Xn i=1 D(sa,st, gi ) (6) Where sa, st represent the speech audio and text features, respectively. Specifically, n is the total duration of the gesture sequence, gi and ˆgi are the ith original gesture and ith generated gesture, respectively. Using MSE (mean squared error) in Equation 3 and continuity loss in Equa… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative results. TABLE IV OBJECTIVE EVALUATION OF PROPOSED MODEL WITH THE STATE-OF-THE-ART. FOR METRICS: CLOSER TO THE GROUND TRUTH IS BETTER. ACCELERATION(ACC.). Model Acc.(cm/s2 ) Jerk(cm/s3 ) RMSE(cm) Gesticulator 63.8 ± 8.3 1332 ± 192 13.0±14.7 Proposed Model 94.48±19.64 2187.76±611.97 4.21±4.54 Ground Truth 144.7 ± 36.6 2322 ± 538 0 B. Subjective Evaluation Our user study was delivered via an anon… view at source ↗
Figure 5
Figure 5. Figure 5: Results of the user study A two-tailed T-test was used to determine if there was a statistically significant difference in the scores of the GT 1Sample from proposed group and sample from GT group 2HC No: HC220411 and proposed groups. Although the mean rating scores of the proposed model are lower than the ground truth, especially in semantic consistency, there was no statistically significant difference a… view at source ↗
read the original abstract

Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread. In human-human interactions, humans use nonverbal behaviours to convey their attitudes, feelings, and intentions. Therefore, this capability is also required for embodied agents in order to enhance the quality and effectiveness of their interactions with humans. In this paper, we propose a novel framework that can generate sequences of joint angles from the speech text and speech audio utterances. Based on a conditional Generative Adversarial Network (GAN), our proposed neural network model learns the relationships between the co-speech gestures and both semantic and acoustic features from the speech input. In order to train our neural network model, we employ a public dataset containing co-speech gestures with corresponding speech audio utterances, which were captured from a single male native English speaker. The results from both objective and subjective evaluations demonstrate the efficacy of our gesture-generation framework for Robots and Embodied Agents.

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 / 0 minor

Summary. The paper proposes a conditional GAN framework that generates sequences of joint angles for co-speech gestures from speech text and audio utterances. The model learns mappings between gestures and semantic/acoustic speech features, trained on a public single-speaker dataset (one male native English speaker), with the authors stating that objective and subjective evaluations demonstrate its efficacy for robots and embodied agents.

Significance. If the central claim holds with adequate evidence, the work could offer a practical approach to generating natural nonverbal behaviors for embodied agents, potentially improving human-agent interaction quality. However, the narrow single-speaker training and evaluation setup provides limited support for the broad applicability claimed.

major comments (2)
  1. [Abstract] Abstract: The claim that the framework demonstrates 'efficacy... for Robots and Embodied Agents' is load-bearing for the paper's contribution, yet all described training and both objective/subjective evaluations occur exclusively on held-out data from a single male native English speaker dataset, with no cross-speaker, cross-gender, or cross-embodiment transfer experiments reported. This directly undermines the generalizability required by the central claim.
  2. [Abstract] Abstract: The description states that the conditional GAN 'learns the relationships between the co-speech gestures and both semantic and acoustic features' and that 'evaluations demonstrate the efficacy,' but supplies no architecture details, loss functions, input feature extraction methods, quantitative metrics, baselines, or error analysis. These omissions make the soundness of the empirical support impossible to assess from the provided text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive comments. We respond to each major comment point by point below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The claim that the framework demonstrates 'efficacy... for Robots and Embodied Agents' is load-bearing for the paper's contribution, yet all described training and both objective/subjective evaluations occur exclusively on held-out data from a single male native English speaker dataset, with no cross-speaker, cross-gender, or cross-embodiment transfer experiments reported. This directly undermines the generalizability required by the central claim.

    Authors: We agree that all training and evaluation in the manuscript are performed on held-out data from a single male native English speaker, as explicitly noted in the abstract and methods. The public dataset used is limited to this speaker, and no cross-speaker, cross-gender, or cross-embodiment experiments are reported. While the conditional GAN framework is designed to learn general mappings from semantic and acoustic speech features to gesture sequences, the current evidence supports efficacy only within this narrow setting. To address the concern, we will revise the abstract to more precisely qualify the scope of the claimed efficacy and explicitly note the single-speaker limitation, thereby aligning the central claim with the reported evidence. revision: partial

  2. Referee: [Abstract] Abstract: The description states that the conditional GAN 'learns the relationships between the co-speech gestures and both semantic and acoustic features' and that 'evaluations demonstrate the efficacy,' but supplies no architecture details, loss functions, input feature extraction methods, quantitative metrics, baselines, or error analysis. These omissions make the soundness of the empirical support impossible to assess from the provided text.

    Authors: The abstract provides a high-level summary of the approach and results, consistent with standard abstract length constraints. Complete details on the model architecture, loss functions, input feature extraction, quantitative metrics, baselines, and error analysis are provided in the full manuscript (Sections 3 and 4). The soundness of the empirical support is therefore assessable from the complete paper rather than the abstract alone. No changes to the abstract are required on this point. revision: no

Circularity Check

0 steps flagged

No circularity; standard conditional GAN trained and evaluated on held-out single-speaker data

full rationale

The paper describes a conditional GAN that learns mappings from speech text/audio features to joint-angle gesture sequences, with training and both objective/subjective evaluations performed on held-out portions of one public single-speaker dataset. No equations, parameter-fitting steps, or derivation chain are presented that reduce a claimed prediction to its own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The approach is a conventional supervised generative model whose outputs are not definitionally equivalent to the training distribution; any limitation on generalizability to other speakers or embodiments is an empirical assumption, not a circularity in the derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the central claim rests on the unstated assumption that the single-speaker dataset is adequate for the stated generalization.

pith-pipeline@v0.9.0 · 5694 in / 1083 out tokens · 23939 ms · 2026-05-24T06:13:19.147901+00:00 · methodology

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Reference graph

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