Consideration of Varying Training Lengths for Short-Duration Speaker Verification

We present an efficient training scheme for speaker verification (SV) networks in short-duration speech input scenarios. We analyze the effects of varying training lengths on SV performance, with a particular focus on short utterances. Despite the high demand for short-duration SV in real-world applications, state-of-the-art SV systems have primarily been evaluated on long utterances, and little research has been conducted on shortduration SV. By considering the innate characteristics of SV architectures and the performance discrepancies associated with varying training data lengths, we propose a training scheme that accounts for varying length conditions. We categorize speaker characteristics as coarse-grained and fine-grained features and demonstrate that training models to learn both features can result in length-robust speaker embeddings. Our proposed training scheme improves model performance by 28.7% and 37.9% in terms of equal error rate on short-duration speech scenarios compared to baseline models.