# Memory-Efficient Training for Text-Dependent SV with Independent Pre-trained Models

arXiv: https://arxiv.org/abs/2411.10828 · HTML: https://mlsanity.com/paper/2411.10828/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 40 references that are in the map.
- First posted on arXiv 16 Nov 2024.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [VoxCeleb2: Deep Speaker Recognition](https://mlsanity.com/paper/1806.05622/) (2018)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Additive Margin Softmax for Face Verification](https://mlsanity.com/paper/1801.05599/) (2018)
- [BUT System Description to VoxCeleb Speaker Recognition Challenge 2019](https://mlsanity.com/paper/1910.12592/) (2019)
- [Utterance-level Aggregation For Speaker Recognition In The Wild](https://mlsanity.com/paper/1902.10107/) (2019)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [Margin Matters: Towards More Discriminative Deep Neural Network Embeddings for Speaker Recognition](https://mlsanity.com/paper/1906.07317/) (2019)
- [The SpeakIn System for VoxCeleb Speaker Recognition Challange 2021](https://mlsanity.com/paper/2109.01989/) (2021)
- [Self Multi-Head Attention for Speaker Recognition](https://mlsanity.com/paper/1906.09890/) (2019)
- [A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database](https://mlsanity.com/paper/1912.03627/) (2019)
- [The DKU-DukeECE Systems for VoxCeleb Speaker Recognition Challenge 2020](https://mlsanity.com/paper/2010.12731/) (2020)
- [SJTU-AISPEECH System for VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.09076/) (2022)
- [Towards A Unified Conformer Structure: from ASR to ASV Task](https://mlsanity.com/paper/2211.07201/) (2022)
- [UNISOUND System for VoxCeleb Speaker Recognition Challenge 2023](https://mlsanity.com/paper/2308.12526/) (2023)
- [Whisper-PMFA: Partial Multi-Scale Feature Aggregation for Speaker Verification using Whisper Models](https://mlsanity.com/paper/2408.15585/) (2024)
- [Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification](https://mlsanity.com/paper/2106.13514/) (2021)
- [Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan](https://mlsanity.com/paper/2404.13428/) (2024)
- [The SJTU System for Short-duration Speaker Verification Challenge 2021](https://mlsanity.com/paper/2208.01933/) (2022)

## Abstract (arXiv)

This paper presents our submission to the Iranian division of the Text-Dependent Speaker Verification Challenge (TdSV) 2024. Conventional TdSV approaches typically jointly model speaker and linguistic features, requiring unsegmented inputs during training and incurring high computational costs. Additionally, these methods often fine-tune large-scale pre-trained speaker embedding models on the target domain dataset, which may compromise the pre-trained models' original ability to capture speaker-specific characteristics. To overcome these limitations, we employ a TdSV system that utilizes two pre-trained models independently and demonstrate that, by leveraging pre-trained models with targeted domain adaptation, competitive results can be achieved while avoiding the substantial computational costs associated with joint fine-tuning on unsegmented inputs in conventional approaches. Our best system reached a MinDCF of 0.0358 on the evaluation subset and secured first place in the challenge.