# HLT-NUS SUBMISSION FOR 2020 NIST Conversational Telephone Speech SRE

arXiv: https://arxiv.org/abs/2111.06671 · HTML: https://mlsanity.com/paper/2111.06671/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Nov 2021.

## Built on by (most cited first, 2 of 2)

- [An End-to-End Multi-Module Audio Deepfake Generation System for ADD Challenge 2023](https://mlsanity.com/paper/2307.00729/) (2023)
- [One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech](https://mlsanity.com/paper/2604.26136/) (2026)

## Abstract (arXiv)

This work provides a brief description of Human Language Technology (HLT) Laboratory, National University of Singapore (NUS) system submission for 2020 NIST conversational telephone speech (CTS) speaker recognition evaluation (SRE). The challenge focuses on evaluation under CTS data containing multilingual speech. The systems developed at HLT-NUS consider time-delay neural network (TDNN) x-vector and ECAPA-TDNN systems. We also perform domain adaption of probabilistic linear discriminant analysis (PLDA) model and adaptive s-norm on our systems. The score level fusion of TDNN x-vector and ECAPA-TDNN systems is carried out, which improves the final system performance of our submission to 2020 NIST CTS SRE.