: In this paper, we introduce a novel method for optimizing enrollment selection in speaker identification systems, with a particular focus on low-resource languages. Unlike traditional approaches that rely on random enrollment samples, our method systematically analyzes pair-wise similarities between enrollment utterances to eliminate poor-quality samples often impacted by noise or adverse environments. By retaining only high-quality and representative utterances, we ensure a more robust speaker profile. This innovative approach, applied to the Vietnam-Celeb dataset using the state-of-the-art ECAPA-TDNN model, delivers substantial performance improvements. Our method boosts accuracy from 73.38% in bad scenarios to 93.62% and increases the F1-score from 72.91% to 95.48%, demonstrating the effectiveness of focusing on quality-driven enrollment selection even in low-resource contexts.
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Data-Centric Optimization of Enrollment Selection in Speaker Identification
Semantic Scholar · Computer Science · 2025
Abstract
: In this paper, we introduce a novel method for optimizing enrollment selection in speaker identification systems, with a particular focus on low-resource languages. Unlike traditional approaches that rely on random enrollment samples, our method systematically analyzes pair-wise similarities between enrollment utterances to eliminate poor-quality samples often impacted by noise or adverse environments. By retaining only high-quality and representative utterances, we ensure a more robust speaker profile. This innovative approach, applied to the Vietnam-Celeb dataset using the state-of-the-art ECAPA-TDNN model, delivers substantial performance improvements. Our method boosts accuracy from 73.38% in bad scenarios to 93.62% and increases the F1-score from 72.91% to 95.48%, demonstrating the effectiveness of focusing on quality-driven enrollment selection even in low-resource contexts.