MERaLiON-GR: Multi-lingual Speech Gender Recognition system that performs bi-nary classification (female / male) on English and Southeast Asian (SEA) languages.

• 🤗 Model: MERaLiON/MERaLiON-GI-v1
• 🎤 Live demo: meralion.org/analysis

🧠 Architecture Overview

• Modality: Speech-only model
• Task: Audio Classification (Gender Recognition)
• Input Sampling Rate: 16,000 Hz
• Output Classes: Female (0), Male (1)

The design achieves parameter-efficient adaptation for multilingual, paralinguistic modeling.

More details on model architecture, training, and evaluation are available in the technical report: arxiv.org/abs/2608.04433

📊 Performance Overview

Public Benchmarks (Accuracy %)

MERaLiON-GR outperforms Vox-Profile on 12 of 15 public test sets and matches it on 1 set.

Singapore Language Performance

In-the-wild 10–30 second Singapore speech segments (evaluated with a 2-second sliding window), MERaLiON-GR consistently outperforms Vox-Profile, with gains up to +4.32 pp (Malay) and +4.16 pp (Tamil).

Baselines

• Vox-Profile: https://huggingface.co/tiantiaf/wavlm-large-age-sex state-of-the-art standalone speech foundation model for gender recognition
• MERaLiON-v2 (MERaLiON-2-10B): general-purpose Audio-LLM, prompted with a fixed instruction to output a gender label

⚙️ Usage Examples

import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoFeatureExtractor

# 1. Load the model and feature extractor
model_id = "MERaLiON/MERaLiON-GR-v1"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
feature_extractor = AutoFeatureExtractor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModel.from_pretrained(model_id, trust_remote_code=True).to(device)
model.eval()

# 2. Make sure your waveformes are 16000 sampling rate
wav, sr = sf.read("sample.wav")
if wav.ndim > 1: wav = wav.mean(axis=1)
if sr != 16000:
    wav = torchaudio.functional.resample(torch.tensor(wav).unsqueeze(0), sr, 16000).squeeze(0).numpy()

inputs = feature_extractor( 
        [wav], sampling_rate=16000, return_tensors="pt", padding=True
    )
inputs = {k: v.to(device) for k, v in inputs.items()}

# inference. The model returns (hidden_states, logits)
with torch.no_grad():
    _, logits = model(**inputs)
        
probs = F.softmax(logits, dim=-1)
pred_class = torch.argmax(probs, dim=-1).item()
    
labels_map = {0: "female", 1: "male"}
print("Predicted gender: {labels_map[pred_class]}")

Citation

If you use this model, please cite the corresponding technical report:

@article{meralion2026gr,
  title={MERaLiON-GR: Speech Gender Recognition Model for English and SEA Languages},
  author={{MERaLiON Team}},
  journal={arXiv preprint arXiv:2608.04433},
  year={2026}
}

Related work referenced in this report:

@inproceedings{wang2025benchmarking,
  title={Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data},
  author={Wang, Qiongqiong and Sailor, Hardik Bhupendra and Liu, Tianchi and Zhang, Wenyu and Huzaifah, Muhammad and Lertcheva, Nattadaporn and Sun, Shuo and Chen, Nancy F and Wu, Jinyang and Aw, AiTi},
  booktitle={Findings of EMNLP 2025},
  year={2025}
}

@inproceedings{cpqa_interspeech,
  title={Contextual Paralinguistic Data Creation for Multi-Modal Speech-LLM: Data Condensation and Spoken {QA} Generation},
  author={Wang, Qiongqiong and Sailor, Hardik B and Liu, Tianchi and Aw, Ai Ti},
  booktitle={Proc. Interspeech},
  year={2025}
}

@inproceedings{cpqa_asru,
  title={Incorporating Contextual Paralinguistic Understanding in Large Speech-Language Models},
  author={Wang, Qiongqiong and Sailor, Hardik B and Wong, Jeremy H. M. and Liu, Tianchi and Sun, Shuo and Zhang, Wenyu and Huzaifah, Muhammad and Chen, Nancy and Aw, Ai Ti},
  booktitle={Proc. IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)},
  year={2025}
}