# Team HYU ASML ROBOVOX SP Cup 2024 System Description

arXiv: https://arxiv.org/abs/2407.11365 · HTML: https://mlsanity.com/paper/2407.11365/

## Key facts

- Cited by 1 papers in the map (data through 19 Oct 2026).
- Placed in Speaker recognition and diarization, one of 11 directions in Speech and audio language models.
- Builds on 26 of its 38 references that are in the map.
- First posted on arXiv 16 Jul 2024.

## Placed in

- Speech and audio language models / [Speaker recognition and diarization](https://mlsanity.com/speech/speaker-recognition-and-diarization/)

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (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)
- [ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification](https://mlsanity.com/paper/2005.07143/) (2020)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [MLS: A Large-Scale Multilingual Dataset for Speech Research](https://mlsanity.com/paper/2012.03411/) (2020)
- [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021)
- [pyannote.audio: neural building blocks for speaker diarization](https://mlsanity.com/paper/1911.01255/) (2019)
- [CN-CELEB: a challenging Chinese speaker recognition dataset](https://mlsanity.com/paper/1911.01799/) (2019)
- [The Multilingual TEDx Corpus for Speech Recognition and Translation](https://mlsanity.com/paper/2102.01757/) (2021)
- [MFA-Conformer: Multi-scale Feature Aggregation Conformer for Automatic Speaker Verification](https://mlsanity.com/paper/2203.15249/) (2022)
- [The IDLAB VoxSRC-20 Submission: Large Margin Fine-Tuning and Quality-Aware Score Calibration in DNN Based Speaker Verification](https://mlsanity.com/paper/2010.11255/) (2020)
- [ArcFace: Additive Angular Margin Loss for Deep Face Recognition](https://mlsanity.com/paper/1801.07698/) (2018)
- [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)
- [Multi-query multi-head attention pooling and Inter-topK penalty for speaker verification](https://mlsanity.com/paper/2110.05042/) (2021)
- [Self-Supervised Speaker Verification Using Dynamic Loss-Gate and Label Correction](https://mlsanity.com/paper/2208.01928/) (2022)
- [A Novel Learnable Dictionary Encoding Layer for End-to-End Language Identification](https://mlsanity.com/paper/1804.00385/) (2018)
- [Non-Contrastive Self-Supervised Learning of Utterance-Level Speech Representations](https://mlsanity.com/paper/2208.05413/) (2022)
- [The Kriston AI System for the VoxCeleb Speaker Recognition Challenge 2022](https://mlsanity.com/paper/2209.11433/) (2022)
- [The ID R&D VoxCeleb Speaker Recognition Challenge 2023 System Description](https://mlsanity.com/paper/2308.08294/) (2023)
- [RSKNet-MTSP: Effective and Portable Deep Architecture for Speaker Verification](https://mlsanity.com/paper/2108.13249/) (2021)
- [The SpeakIn Speaker Verification System for Far-Field Speaker Verification Challenge 2022](https://mlsanity.com/paper/2209.11625/) (2022)
- [VACE-WPE: Virtual Acoustic Channel Expansion Based On Neural Networks for Weighted Prediction Error-Based Speech Dereverberation](https://mlsanity.com/paper/2102.05259/) (2021)
- [Task-specific Optimization of Virtual Channel Linear Prediction-based Speech Dereverberation Front-End for Far-Field Speaker Verification](https://mlsanity.com/paper/2112.13569/) (2021)

## Built on by (most cited first, 1 of 1)

- [Trainable Adaptive Score Normalization for Automatic Speaker Verification](https://mlsanity.com/paper/2504.04512/) (2025)

## Abstract (arXiv)

This report describes the submission of HYU ASML team to the IEEE Signal Processing Cup 2024 (SP Cup 2024). This challenge, titled "ROBOVOX: Far-Field Speaker Recognition by a Mobile Robot," focuses on speaker recognition using a mobile robot in noisy and reverberant conditions. Our solution combines the result of deep residual neural networks and time-delay neural network-based speaker embedding models. These models were trained on a diverse dataset that includes French speech. To account for the challenging evaluation environment characterized by high noise, reverberation, and short speech conditions, we focused on data augmentation and training speech duration for the speaker embedding model. Our submission achieved second place on the SP Cup 2024 public leaderboard, with a detection cost function of 0.5245 and an equal error rate of 6.46%.