Summary of Enabling Asr For Low-resource Languages: a Comprehensive Dataset Creation Approach, by Ara Yeroyan (data Science Department et al.
Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach
by Ara Yeroyan, Nikolay Karpov
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Audio and Speech Processing (eess.AS); Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel pipeline is introduced to generate automatic speech recognition (ASR) training datasets from audiobooks, addressing the challenge of preparing datasets for ASR systems in low-resource languages. The proposed method aligns audio with text and segments it into suitable lengths for ASR training, simplifying data preparation. A case study involving Armenian language demonstrates the approach’s application. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to help computers understand speech from books is being developed. Right now, computers are really good at understanding speech in many languages. But they struggle with languages that don’t have as much audio and text data. This project makes it easier for computers to learn to recognize speech by taking audio books and breaking them down into smaller pieces that can be used to train the computer. |
