Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias

• URL: http://arxiv.org/abs/2009.09556v1
• Date: Mon, 21 Sep 2020 00:58:40 GMT
• Title: Open-set Short Utterance Forensic Speaker Verification using Teacher-Student Network with Explicit Inductive Bias
• Authors: Mufan Sang, Wei Xia, John H.L. Hansen
• Abstract summary: We propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning. We show that the proposed objective function can efficiently improve the performance of teacher-student learning on short utterances.
• Score: 59.788358876316295
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In forensic applications, it is very common that only small naturalistic datasets consisting of short utterances in complex or unknown acoustic environments are available. In this study, we propose a pipeline solution to improve speaker verification on a small actual forensic field dataset. By leveraging large-scale out-of-domain datasets, a knowledge distillation based objective function is proposed for teacher-student learning, which is applied for short utterance forensic speaker verification. The objective function collectively considers speaker classification loss, Kullback-Leibler divergence, and similarity of embeddings. In order to advance the trained deep speaker embedding network to be robust for a small target dataset, we introduce a novel strategy to fine-tune the pre-trained student model towards a forensic target domain by utilizing the model as a finetuning start point and a reference in regularization. The proposed approaches are evaluated on the 1st48-UTD forensic corpus, a newly established naturalistic dataset of actual homicide investigations consisting of short utterances recorded in uncontrolled conditions. We show that the proposed objective function can efficiently improve the performance of teacher-student learning on short utterances and that our fine-tuning strategy outperforms the commonly used weight decay method by providing an explicit inductive bias towards the pre-trained model.

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