Bilingual Text-dependent Speaker Verification with Pre-trained Models for TdSV Challenge 2024

• URL: http://arxiv.org/abs/2411.10828v1
• Date: Sat, 16 Nov 2024 15:53:03 GMT
• Title: Bilingual Text-dependent Speaker Verification with Pre-trained Models for TdSV Challenge 2024
• Authors: Seyed Ali Farokh,
• Abstract summary: We present our submissions to the Iranian division of the Text-dependent Speaker Verification Challenge (TdSV) 2024. TdSV aims to determine if a specific phrase was spoken by a target speaker. For phrase verification, a phrase rejected incorrect phrases, while for speaker verification, a pre-trained ResNet293 with domain adaptation extracted speaker embeddings. Whisper-PMFA, a pre-trained ASR model adapted for speaker verification, falls short of the performance of pre-trained ResNets.
• Score: 0.0
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• Abstract: This paper presents our submissions to the Iranian division of the Text-dependent Speaker Verification Challenge (TdSV) 2024. TdSV aims to determine if a specific phrase was spoken by a target speaker. We developed two independent subsystems based on pre-trained models: For phrase verification, a phrase classifier rejected incorrect phrases, while for speaker verification, a pre-trained ResNet293 with domain adaptation extracted speaker embeddings for computing cosine similarity scores. In addition, we evaluated Whisper-PMFA, a pre-trained ASR model adapted for speaker verification, and found that, although it outperforms randomly initialized ResNets, it falls short of the performance of pre-trained ResNets, highlighting the importance of large-scale pre-training. The results also demonstrate that achieving competitive performance on TdSV without joint modeling of speaker and text is possible. Our best system achieved a MinDCF of 0.0358 on the evaluation subset and won the challenge.

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