# Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset

arXiv: https://arxiv.org/abs/2006.08274 · HTML: https://mlsanity.com/paper/2006.08274/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jun 2020.

## Built on by (most cited first, 6 of 6)

- [MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research](https://mlsanity.com/paper/2406.18301/) (2024)
- [Automatic Speech Recognition with BERT and CTC Transformers: A Review](https://mlsanity.com/paper/2410.09456/) (2024)
- [Uconv-Conformer: High Reduction of Input Sequence Length for End-to-End Speech Recognition](https://mlsanity.com/paper/2208.07657/) (2022)
- [BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition](https://mlsanity.com/paper/2505.00059/) (2025)
- [A Study of Multilingual End-to-End Speech Recognition for Kazakh, Russian, and English](https://mlsanity.com/paper/2108.01280/) (2021)
- [Dynamic Acoustic Unit Augmentation With BPE-Dropout for Low-Resource End-to-End Speech Recognition](https://mlsanity.com/paper/2103.07186/) (2021)

## Abstract (arXiv)

This paper presents an exploration of end-to-end automatic speech recognition systems (ASR) for the largest open-source Russian language data set -- OpenSTT. We evaluate different existing end-to-end approaches such as joint CTC/Attention, RNN-Transducer, and Transformer. All of them are compared with the strong hybrid ASR system based on LF-MMI TDNN-F acoustic model. For the three available validation sets (phone calls, YouTube, and books), our best end-to-end model achieves word error rate (WER) of 34.8%, 19.1%, and 18.1%, respectively. Under the same conditions, the hybridASR system demonstrates 33.5%, 20.9%, and 18.6% WER.