A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database

TL;DR

DeepMine is a comprehensive large-scale speech database in Persian and English, enabling advancements in speaker verification and speech recognition with diverse evaluation protocols and baseline results.

Contribution

It introduces the first large-scale Persian speaker verification database and the largest English text-dependent and prompt-based speaker verification dataset.

Findings

HMM-based i-vectors achieve competitive speaker verification performance.

Deep neural network ASR models trained on DeepMine show robustness.

The database covers diverse demographics for comprehensive research.

Abstract

DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.