# STC Speaker Recognition Systems for the VOiCES From a Distance Challenge

## TL;DR

This paper describes STC's speaker recognition systems for the VOiCES 2019 challenge, focusing on deep neural network architectures, data augmentation, and fusion techniques to improve recognition in noisy, far-field conditions.

## Contribution

The work introduces residual neural network architectures and robust data augmentation methods for improved speaker embedding extraction in challenging acoustic environments.

## Key findings

- Deep residual networks outperform shallow models.
- Data augmentation with room impulse responses enhances robustness.
- Fusion of multiple subsystems yields better recognition accuracy.

## Abstract

This paper presents the Speech Technology Center (STC) speaker recognition (SR) systems submitted to the VOiCES From a Distance challenge 2019. The challenge's SR task is focused on the problem of speaker recognition in single channel distant/far-field audio under noisy conditions. In this work we investigate different deep neural networks architectures for speaker embedding extraction to solve the task. We show that deep networks with residual frame level connections outperform more shallow architectures. Simple energy based speech activity detector (SAD) and automatic speech recognition (ASR) based SAD are investigated in this work. We also address the problem of data preparation for robust embedding extractors training. The reverberation for the data augmentation was performed using automatic room impulse response generator. In our systems we used discriminatively trained cosine similarity metric learning model as embedding backend. Scores normalization procedure was applied for each individual subsystem we used. Our final submitted systems were based on the fusion of different subsystems. The results obtained on the VOiCES development and evaluation sets demonstrate effectiveness and robustness of the proposed systems when dealing with distant/far-field audio under noisy conditions.