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

arXiv: https://arxiv.org/abs/1912.03627 · HTML: https://mlsanity.com/paper/1912.03627/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [ParsVoice: A Large-Scale Multi-Speaker Persian Speech Corpus for Text-to-Speech Synthesis](https://mlsanity.com/paper/2510.10774/) (2025)
- [Text-dependent Speaker Verification (TdSV) Challenge 2024: Challenge Evaluation Plan](https://mlsanity.com/paper/2404.13428/) (2024)
- [PSRB: A Comprehensive Benchmark for Evaluating Persian ASR Systems](https://mlsanity.com/paper/2505.21230/) (2025)
- [Class Token and Knowledge Distillation for Multi-head Self-Attention Speaker Verification Systems](https://mlsanity.com/paper/2111.03842/) (2021)
- [Where Are You From? Let Me Guess! Subdialect Recognition of Speeches in Sorani Kurdish](https://mlsanity.com/paper/2404.00124/) (2024)
- [Asymmetric and trial-dependent modeling: the contribution of LIA to SdSV Challenge Task 2](https://mlsanity.com/paper/2403.19634/) (2024)
- [Memory-Efficient Training for Text-Dependent SV with Independent Pre-trained Models](https://mlsanity.com/paper/2411.10828/) (2024)
- [Text-Dependent Speaker Verification (TdSV) Challenge 2024: Team Naive System Report](https://mlsanity.com/paper/2605.14896/) (2026)
- [The Annotation Bottleneck in Persian Text NLP: Persian as an Annotation-Scarce Language](https://mlsanity.com/paper/2608.24698/) (2026)
- [Neyshekar: An Open Persian Read-Speech Corpus for Automatic Speech Recognition](https://mlsanity.com/paper/2609.14542/) (2026)

## Abstract (arXiv)

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.