Signer-Invariant Conformer in CSLR

• The paper introduces a pose-based Conformer tailored for signer-independent CSLR using torso-normalized skeletal keypoints, achieving a test WER of 13.07%.
• The model integrates local temporal convolutions with global self-attention to capture fine gestural details and long-range sentence context.
• The innovative preprocessing pipeline effectively suppresses inter-signer variability caused by differences in body proportions, camera distance, and motion habits.

Searching arXiv for the specified paper and closely related CSLR work to ground the article. The Signer-Invariant Conformer is a pose-based Conformer encoder for Signer-Independent (SI) continuous sign language recognition (CSLR), introduced in "A Signer-Invariant Conformer and Multi-Scale Fusion Transformer for Continuous Sign Language Recognition" (Haque et al., 12 Aug 2025). It is designed to learn representations that are “independent of signer characteristics” from pose-based skeletal keypoints, with the explicit goal of suppressing inter-signer variability arising from differences in style, body proportions, camera distance, and motion habits. In the reported system, signer invariance is pursued through a combination of skeletal representation instead of RGB, normalization relative to the torso bounding box, local temporal convolutions, multi-head self-attention, and CTC-based weak alignment rather than through a separate explicit invariance loss (Haque et al., 12 Aug 2025).

1. Problem setting and motivation

Within the paper’s formulation, the Signer-Invariant Conformer addresses the Signer-Independent (SI) benchmark on Isharah-1000, where the principal difficulty is that the same sign may vary across signers because of hand and body size differences, signing speed, motion style, camera distance and viewpoint, occlusion and blur, and co-articulation across continuous signs (Haque et al., 12 Aug 2025). The model is therefore positioned as a task-specific architecture for SI CSLR rather than as a generic sequence model.

The stated motivation is that standard sequence models often fail to suppress this variability efficiently. The authors select a Conformer-style encoder because it mixes convolutions for short-range or local gesture patterns with self-attention for long-range temporal dependencies. In their framing, this hybrid is especially suitable for continuous signing, where both local posture transitions and sentence-level context matter (Haque et al., 12 Aug 2025).

A central design decision is the use of pose-based skeletal keypoints rather than raw RGB. This removes much of the appearance variation associated with signer identity and shifts the learning problem toward motion and articulation structure. This suggests that the Signer-Invariant Conformer should be understood less as a mathematically novel Conformer variant than as a task-tailored Conformer pipeline for pose-based SI CSLR.

2. Input representation and preprocessing

The model operates on 2D pose landmarks extracted from each video frame, including pose, hands, and face (Haque et al., 12 Aug 2025). Each frame is represented by $K = 86$ landmarks, with each landmark given by $(x,y)$ coordinates. The raw sequence is written as

$$X = \{\mathbf{x}_1, \mathbf{x}_2, \dots, \mathbf{x}_T\},$$

where $T$ is the number of frames and each $\mathbf{x}_t \in \mathbb{R}^{K \times 2}$ (Haque et al., 12 Aug 2025).

Preprocessing is explicitly tied to the signer-invariant objective. Coordinates are normalized relative to a torso bounding box so that scale and position are more consistent across signers. Missing keypoints from occlusion or blur are linearly interpolated from adjacent frames. The resulting sequence is then flattened into

$${X}' \in \mathbb{R}^{T \times D}, \quad D = K \times 2.$$

The paper treats this preprocessing as central rather than incidental. By reducing nuisance variation before sequence modeling begins, the system attempts to make downstream temporal features more stable across unseen signers (Haque et al., 12 Aug 2025).

This preprocessing also distinguishes the model from pipelines centered on appearance features or graph-first skeleton encoders. The relevant point is not merely that pose is used, but that pose is normalized and regularized in a way specifically aligned with the SI setting.

3. Architectural composition

The overall pipeline is described as follows: extract pose keypoints from each frame; normalize them; pass the sequence through a shallow temporal encoder of 1D convolutions; feed the features into a stack of Conformer blocks; use a layer-normalized linear classifier head to predict glosses; and train with CTC loss (Haque et al., 12 Aug 2025).

The shallow temporal encoder is composed of 1D convolutional layers, batch normalization, and ReLU activations. Its role is to project raw keypoint coordinates into a better feature space while capturing short-range local correlations across nearby frames (Haque et al., 12 Aug 2025). Before entering the Conformer stack, the sequence is augmented with sinusoidal positional encodings, which provide ordering information.

The main encoder consists of Conformer blocks combining multi-head self-attention (MHSA), depthwise convolution, feed-forward layers, residual connections, and layer normalization (Haque et al., 12 Aug 2025). The attention submodule is defined in the paper as

$$\text{MHSA}(Z) = F_{concat}(\text{head}_1, \dots, \text{head}_h)W^O$$

with

$$\text{head}_i = F_{Attention}(ZW_i^Q, ZW_i^K, ZW_i^V) = F_{softmax}\left(\frac{(ZW_i^Q)(ZW_i^K)^T}{\sqrt{d_k}}\right)ZW_i^V .$$

In the authors’ interpretation, MHSA captures global, long-range dependencies and helps model sentence-level context and co-articulation (Haque et al., 12 Aug 2025).

After attention, each block includes a convolutional branch consisting of a 1D depthwise convolution layer with GLU (Gated Linear Unit) activation. The paper states that this branch captures local, relative positional information and fine-grained gestural patterns that attention may miss. The outputs of the attention and convolution modules are then integrated through residual connections and layer normalization, which is the defining hybrid structure of the Conformer in this setting (Haque et al., 12 Aug 2025).

The final classifier head applies layer normalization followed by a linear layer projecting into the gloss vocabulary of size $|V_g|$. This establishes the model as a visual-sequence recognizer operating directly over normalized keypoint sequences rather than over appearance embeddings or graph-structured skeleton states.

4. Objective function and training protocol

The Signer-Invariant Conformer is trained with CTC loss, which the paper motivates by the absence of explicit frame-level alignment in CSLR (Haque et al., 12 Aug 2025). The text states that CTC is appropriate because it sums over all possible alignments that yield the correct gloss sequence. The model therefore predicts glosses at the sequence level without requiring frame-by-frame annotation.

Evaluation is reported with Word Error Rate (WER):

$\text{WER} = \frac{S + I + D}{N},$

where $(x,y)$0 denotes substitutions, $(x,y)$1 insertions, $(x,y)$2 deletions, and $(x,y)$3 the number of reference glosses (Haque et al., 12 Aug 2025). Lower WER indicates better performance.

The experiments use Isharah-1000, described as a subset of the Isharah dataset for Continuous Saudi Sign Language recognition. The dataset details given in the paper are: 15,000 videos, 1,000 unique sign-language sentences, 18 signers, gloss vocabulary: 685, text vocabulary: 1,496, and videos recorded on smartphones in unconstrained real-world environments (Haque et al., 12 Aug 2025). For the SI split, the test set contains videos from four signers not present in training or development.

The implementation is in PyTorch and uses AdamW, a learning rate of $(x,y)$4, cosine annealing, batch size 16, training for 100 to 675 epochs, and two NVIDIA A6000 GPUs (Haque et al., 12 Aug 2025). These details situate the model within a standard modern optimization regime, but the paper attributes its performance primarily to representation and architecture choices rather than to an unusual training recipe.

5. Comparative position among related architectures

The paper does not present the Signer-Invariant Conformer as an entirely new Conformer formalism; instead, it emphasizes differences in input modality, preprocessing, and application focus (Haque et al., 12 Aug 2025). Standard Conformer use is contrasted implicitly with this model’s adaptation to pose keypoint sequences for CSLR.

Several comparisons clarify the model’s place in the broader design space. Relative to a more generic Sign-Conformer, the Signer-Invariant Conformer is the authors’ tailored SI architecture and performs substantially better. Relative to ST-GCN-Conformer, which first applies graph convolutions over the skeleton graph and then a conformer, the proposed model works directly with normalized flattened keypoints plus temporal convolution and Conformer blocks. Relative to BiLSTM and CNN-BiLSTM, it replaces recurrent temporal modeling with a hybrid that explicitly combines local convolutional modeling and global self-attention. The paper also reports LLM-SlowFast, LLaMA-Former, LLaMA-SlowFast, and Mamba-Sign among the baselines, while stating that the LLM-based baselines are not effective for SI CSLR relative to a strong pose-based visual-sequence model (Haque et al., 12 Aug 2025).

The following summary organizes the distinctions stated in the paper:

Comparison target	Characterization in the paper	Contrast with Signer-Invariant Conformer
Sign-Conformer	More generic conformer baseline	Proposed model is task-tailored for SI CSLR
ST-GCN-Conformer	Graph convolutions over skeleton graph followed by conformer	Proposed model uses normalized flattened keypoints, temporal convolution, and Conformer blocks
BiLSTM / CNN-BiLSTM	Recurrent temporal modeling	Proposed model adds explicit long-range attention and hybrid local/global modeling
LLM-based baselines	Inject LLMs	Reported as less effective for SI CSLR than the pose-based visual-sequence model

A plausible implication is that the paper treats pose normalization plus hybrid temporal modeling as more consequential for signer-independent recognition than augmenting CSLR with language-model priors. That implication is supported by the comparative framing, but the paper does not provide a formal component-wise ablation table for the Signer-Invariant Conformer itself (Haque et al., 12 Aug 2025).

6. Empirical results, interpretation, and scope

On the Signer-Independent benchmark, the Signer-Invariant Conformer achieves Dev WER: 7.31% and Test WER: 13.07%, which the paper identifies as the best SI result in its comparison table (Haque et al., 12 Aug 2025). The prior best published result is reported as Swin-MSTP: 26.6% test WER. The paper therefore states an improvement of 13.53 absolute WER points over the previous best and notes more than 50% relative error reduction.

The paper also reports that CNN-BiLSTM attains 22.62% test WER, which the authors interpret as evidence that a strong pose front-end is crucial (Haque et al., 12 Aug 2025). Within that interpretation, the Signer-Invariant Conformer’s additional gain is attributed to the combination of local convolution for gestural details, global self-attention for context, and pose normalization for signer robustness.

A concise summary of the explicit SI numbers is as follows:

Model/result	WER
Signer-Invariant Conformer, dev	7.31%
Signer-Invariant Conformer, test	13.07%
Swin-MSTP, test	26.6%
CNN-BiLSTM, test	22.62%

In the SignEval 2025 CSLR challenge, the team placed 4th in the SI task and 2nd in the US task, where the US result pertains to the paper’s separate Multi-Scale Fusion Transformer rather than to the Signer-Invariant Conformer (Haque et al., 12 Aug 2025). This distinction is important because the paper’s dual-architecture framework assigns different models to different failure modes: the Signer-Invariant Conformer addresses SI, whereas the Multi-Scale Fusion Transformer addresses Unseen-Sentences (US).

The central claim made for the Signer-Invariant Conformer is that task-specific pose modeling can produce signer-invariant CSLR representations. More specifically, the model is claimed to be robust because it uses skeletal keypoints to remove irrelevant appearance variation, torso-based normalization to align scale and position, and joint local-global temporal modeling to improve generalization to unseen signers (Haque et al., 12 Aug 2025). The reported SI result is presented as empirical support for that claim.

At the same time, the paper does not report a formal component-wise ablation table for the Signer-Invariant Conformer. Consequently, the evidence for individual design choices is comparative rather than isolating. This suggests that the strongest conclusion supported by the reported data is about the effectiveness of the complete pose-based Conformer pipeline on Isharah-1000 SI CSLR, rather than about the independently quantified contribution of each architectural element.