MERaLiON-GR: Speech Gender Recognition Model for English and SEA Languages
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Abstract (arXiv)
We present MERaLiON-GR, a speech gender recognition system that performs binary classification (female / male) on English and Southeast Asian (SEA) languages. The model finetunes MERaLiON-SpeechEncoder-2, a large conformer based transformer pre-trained on a broad speech corpus, and applies parameter efficient fine-tuning via Low-Rank Adaptation (LoRA) to adapt the encoder to the gender recognition task, and appends a multi-scale ECAPA-TDNN down stream network with attention pooling and a lightweight linear classifier. Extensive evaluations across multilingual Singaporean and Southeast Asian languages (English, Chinese, Malay, Tamil, Thai, Vietnamese, Indonesian, and Khmer) show that MERaLiON-GR consistently surpasses the state-of-the-art gender recognition model Vox-Profile and a large Audio-LLM, in both full-utterance and segment level evaluation modes. The results underscore the value of dedicated speech models in achieving accurate paralinguistic understanding and strong cross-lingual generalization.
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References · 15
- 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,853
- 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,301
- 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,609
- 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,358
- 2019Class-Balanced Loss Based on Effective Number of Samplescited by 1,239
- 2018VoxCeleb2: Deep Speaker Recognitioncited by 862
- 2017VoxCeleb: a large-scale speaker identification datasetcited by 714
- 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 606
- 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 458
- 2023A Rank Stabilization Scaling Factor for Fine-Tuning with LoRAcited by 231
- 2025Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Datacited by 10
- 2025Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Modelscited by 8
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