Voice-to-Sign Translation System

• Voice-to-Sign Component is a multimodal system that translates live speech into sign language using integrated neural and rule-based pipelines.
• It combines real-time ASR, gloss decoding, and motion rendering to produce expressive, accessible sign language visualizations.
• Current research focuses on reducing latency, enhancing motion realism, and enabling user edits for improved accessibility.

A Voice-to-Sign component is a multimodal translation system that converts spoken language input—typically a live audio stream—into sign language representations, using end-to-end machine learning, explicit linguistic modeling, or hybrid pipelines. Such systems support accessibility and human-computer interaction for Deaf and Hard-of-Hearing users by providing real-time, naturalistic sign-language visualizations or animations that are faithful to the meaning and prosody of the speech input. Recent advances integrate speech recognition, linguistic analysis, and fine-grained motion synthesis, often with editability and human-in-the-loop optimization.

1. Architectural Paradigms in Voice-to-Sign Systems

Voice-to-Sign components generally comprise the following high-level stages:

• Speech Recognition (ASR): Audio input is processed in real time via feature extraction (e.g., log-Mel or MFCCs) and decoded to text using models such as Whisper, Google Web Speech API, or custom Conformer/Transformer-based acoustic models (Rahman et al., 9 Jul 2025, Revankar et al., 6 Dec 2025, Li, 17 Jun 2025, Rastgoo et al., 2022).
• Intermediate Representation: Transcripts may be further normalized and processed into gloss sequences, which serve as sign language-specific tokenizations omitting spoken grammar and mapping to equivalent sign morphs (Rahman et al., 9 Jul 2025, Rastgoo et al., 2022).
• Gloss/Pose Decoding: Gloss tokens are converted into 3D keypoint trajectories or motion latents, often using Transformer-based sequence-to-sequence generation, optionally with adversarial training or Mixture Density Network (MDN) output heads (Li, 17 Jun 2025, Kapoor et al., 2021).
• Animation and Rendering: 3D motion is retargeted onto avatars (via Unity3D Two-Bone IK, spline interpolation, or GAN-based video synthesis), producing visually smooth and expressive animations (Li, 17 Jun 2025, Rahman et al., 9 Jul 2025, Rastgoo et al., 2022).
• Editability and User Interaction: Modern architectures decouple the motion generation and rendering stages via human-editable intermediate representations, such as a JSON-centric layer that encodes gloss, motion, and facial articulators, allowing granular manual correction and personalization (Li, 17 Jun 2025).
• Low-resource and Lightweight Modes: Systems targeting edge deployment (e.g., Sanvaad) use cloud-based ASR and predefined GIF libraries or alphabet visualization, enabling sub-second latency using minimal compute (Revankar et al., 6 Dec 2025).

The table below summarizes the principal stages and distinguishing features in selected systems:

System	ASR/Frontend	Intermediate	Motion Generation	Rendering	Editability/User Loop
(Li, 17 Jun 2025)	Streaming Conformer	Editable JSON gloss/latent	Transformer-MDN (3D latent)	Unity3D + smoothing	Yes: JSON+resampling
(Rahman et al., 9 Jul 2025)	Whisper	MarianMT gloss sequence	3D keypoint retrieval/spline	3D skeleton animation	No
(Revankar et al., 6 Dec 2025)	Google Web Speech	Phrase/alphabet mapping	Pre-recorded GIF/alphabet image	GIF/PNG visualization	No
(Kapoor et al., 2021)	Mel + Transformer	None or aux. text/gloss	Multi-task Transformer+GAN	Skeleton pose to avatar	No

2. Neural and Statistical Methods

Modern Voice-to-Sign components rely heavily on neural architectures for robust speech understanding, high-level language modeling, and motion synthesis. The main patterns include:

• Streaming Conformer Encoders: These process speech features causally in real time, combining local convolution for short-term prosodic detail and multi-headed self-attention (MHSA) for long-range integration (Li, 17 Jun 2025).
  • For Layer ℓ, input $u_t^{(\ell-1)}$ is transformed as:

  $$u_t^{(\ell)} = \mathrm{LayerNorm}\bigl(u_t^{(\ell-1)} + \ldots + \text{MHSA}_\text{causal}(u_t^{(\ell-1)}) + \ldots\bigr)$$

• Autoregressive Decoders with Mixture Density Networks (MDN): For gestural motion, the decoder predicts a distribution over next latent $z_t$ given past motion, speech-derived embedding $H$, and an MDN with $K$ components:

$$p(z_t\mid z_{<t},H) = \sum_{k=1}^K \pi_{t,k}\,\mathcal{N}(z_t; \mu_{t,k}, \sigma_{t,k}^2 I)$$

Loss is a weighted sum of MDN negative log-likelihood, gloss cross-entropy, and Action Unit focal loss (Li, 17 Jun 2025).

• Auxiliary Tasks and Multi-task Learning: Some systems co-train speech-to-text and gesture generation branches sharing the encoder, improving semantic alignment (Kapoor et al., 2021).

• Adversarial Training: Pose discriminators penalize unnatural outputs at the frame or sequence level; text/gloss–pose matching is regularized by cross-modal GAN losses (Kapoor et al., 2021, Rastgoo et al., 2022).

• Rule-based and NMT Gloss Generation: For text-based intermediate representations, Transformer-based NMT models (e.g., MarianMT) are trained on synthetic English-gloss parallel corpora (BookGlossCorpus-CG), sometimes augmented with semantic similarity reranking via FastText or Word2Vec (Rahman et al., 9 Jul 2025).

  • BLEU-1 and BLEU-2 for gloss translation reach 0.7714 and 0.8923, respectively, with MarianMT (Rahman et al., 9 Jul 2025).
• Hybrid and Lookup-based Approaches: Lightweight systems use phrase-matching to select pre-recorded sign GIFs; for out-of-vocabulary cases, alphabet-based visualizations are displayed (Revankar et al., 6 Dec 2025).

3. Intermediate Representations, Editability, and User Control

Intermediate layers between ASR and avatar animation serve to expose the system’s internal decisions for inspection, correction, or adaptation:

• Editable JSON Arrays: Each sign-unit is expressed as a JSON object encapsulating gloss, start/end times, handshape, movement, expression, syntax tag, and relevant latent indices (Li, 17 Jun 2025). The schema is:

{ "gloss": "STRING", "start": 0.00, "end": 0.82, "handshape": "ENUM", "movement": "ENUM", "expression": "ENUM", "syntax_tag": "ENUM", "latent_indices": [1,2,3,4] }

• Partial Latent Resampling: On edit, a “Resampling Hook” is invoked, reinjecting the edited latent and recomputing only subsequent frames, ensuring sub-100 ms re-rendering (Li, 17 Jun 2025). Pseudocode for partial decoding:

on user_edit(at time τ, new_latent ẑ_τ): z[τ] = ẑ_τ for t in τ+1…T: z[t] = sample_mixture(...)

• Phrase-to-GIF/Alphabet Mapping: Systems targeting casual or resource-limited deployments use an indexed phrase-to-asset mapping, with fallback to letter visualization for unknown phrases (Revankar et al., 6 Dec 2025).

User-facing control mechanisms (e.g., explicit editing of sign gloss, motion trajectory, or facial expression; post-edit reward rating) are unique to the most recent architectures (Li, 17 Jun 2025). This enables real-time, end-user or expert adaptation of system output.

4. Performance Metrics and Results

Performance is evaluated along several axes, including neural inference speed, animation fluency, semantic alignment, and human usability. Salient quantitative results include:

• Latency: State-of-the-art neural architectures achieve end-to-end avatar latencies of 103 ± 6 ms on RTX 4070-class GPUs (TensorRT-optimized, INT8 quantized) (Li, 17 Jun 2025). Feature extraction, Conformer encoding, neural decoding, and rendering are each profiled separately (per-frame neural inference 13 ms).
• Motion Quality: Probability of Correct Keypoints ([email protected]) and Dynamic Time Warping (DTW) scores are reported for ISL gesture trajectories. Performance leading to PCK=53.30 and DTW=14.05 (lower is better) is obtained with cross-modal discriminators (Kapoor et al., 2021).
• Gloss Translation: BLEU-1/2 of 0.7714/0.8923 with MarianMT on synthetic gloss corpora (BookGlossCorpus-CG) (Rahman et al., 9 Jul 2025).
• User-Centric Metrics: System Usability Scale (SUS) improvement of +13 points, 6.7-point cognitive load reduction, and gains in trust and naturalness (p<0.001) versus baseline, evaluated with 20 Deaf signers and 5 interpreters (Li, 17 Jun 2025).
• Resource-Constrained Performance: End-to-end latency for phrase-based V2S modules in Sanvaad is 450 ms (mic→visual output), with phrase-matching accuracy of 95% over 500 utterances on CPU-only hardware (Revankar et al., 6 Dec 2025).

5. Data Resources and Training Paradigms

Effective Voice-to-Sign models require parallel speech, text, gloss, and sign video datasets. Key resources include:

• Synthetic Parallel Corpora: BookGlossCorpus-CG: 1.3M English-ASL gloss pairs, generated by rule-based grammar transformation on BookCorpus sentences (Rahman et al., 9 Jul 2025).
• Sign Gesture Datasets: Sign3D-WLASL (1,983 isolated signs, 133 keypoints), RWTH-Phoenix-2014T, How2Sign, and custom ISL datasets are leveraged for motion modeling and evaluation (Rahman et al., 9 Jul 2025, Rastgoo et al., 2022, Kapoor et al., 2021).
• Speech and Pose Annotations: 3D pose is derived from video using OpenPose or similar lifting algorithms; speech is segmented to match gesture sequences for alignment (Kapoor et al., 2021).

Training leverages multi-task objectives:

$$\mathcal{L}_{\text{Total}} = \lambda_{\text{Reg}}\,L_{\text{Reg}} + \lambda_{\text{Xent}}\,L_{\text{Xent}} + \lambda_{\text{GAN}}\,L_{\text{GAN}}$$

with typical weightings λ_ values selected for balance (Kapoor et al., 2021).

Continuous improvement is obtained via periodic human-in-the-loop fine-tuning. Edits and Likert-scale ratings are logged and used to update decoder parameters using KL-regularized policy gradients:

$$J(\theta) = \mathbb{E}_{\pi_\theta} \Bigl[ \sum_{t=0}^{\infty} \gamma^t (w_u r_u + w_e r_e) \Bigr] - \beta D_{KL}(\pi_\theta \|\pi_{\theta_{\rm old}})$$

(Li, 17 Jun 2025).

6. Applications, Deployment, and Challenges

Deployed Voice-to-Sign systems provide key infrastructure for accessible, two-way communication between Deaf and hearing users in diverse contexts:

• Real-time AV Interfaces: Integrated pipelines are deployed inside desktop/mobile accessibility frameworks (Sanvaad via Streamlit; Unity3D for avatar rendering) (Revankar et al., 6 Dec 2025, Li, 17 Jun 2025).
• Edge and Resource-Limited Systems: Efficient phrase-matching and visualization using pre-recorded GIFs or alphabet signs, no local deep learning inference, suitable for CPU-bound hardware (Revankar et al., 6 Dec 2025).
• Research Prototypes vs. Production: End-to-end, editable neural frameworks (e.g., (Li, 17 Jun 2025)) deliver high naturalness, expressivity, and user adaptation, establishing new usability baselines but requiring modern GPU hardware and calibrated training data.
• Open Challenges: Persistent problems include signer and language diversity, data scarcity for low-resource and non-European sign languages, full modeling of non-manual articulators (face, gaze, mouth patterns), real-time low-latency deployment, and advancing motion realism beyond keypoints to mesh- or neural-avatar synthesis (Rastgoo et al., 2022).

Table: Reported Latency and Evaluation Figures (selected)

System	End-to-End Latency	Usability (SUS gain)	[email protected]	BLEU-1 (Gloss)
(Li, 17 Jun 2025)	103 ± 6 ms	+13	—	—
(Revankar et al., 6 Dec 2025)	450 ms	—	—	—
(Kapoor et al., 2021)	< 300 ms	—	53.30	—
(Rahman et al., 9 Jul 2025)	—	—	—	0.7714

7. Research Directions and Outlook

Emergent research targets fully editable, human-centered multimodal systems fusing high-fidelity neural translation with actionable intermediate representations for both expert and end-user intervention. Policy-gradient–based reward optimization closes the usability gap and maintains grammaticality in evolving deployments (Li, 17 Jun 2025). Synthesis of smooth, expressive motion is advanced by GANs, MDN heads, and sequence-level discriminators (Kapoor et al., 2021, Rastgoo et al., 2022). Large-scale synthetic parallel datasets, multi-modal embedding alignment, and lightweight designs for edge devices are simultaneously expanding coverage and democratizing practical deployment (Rahman et al., 9 Jul 2025, Revankar et al., 6 Dec 2025).

A plausible implication is that next-generation Voice-to-Sign components will increasingly combine real-time neural translation, transparent explainability, and interactive correction, thus supporting widespread, inclusive accessibility for Deaf and Hard-of-Hearing communities across global sign languages.