Summary of A New Benchmark For Evaluating Automatic Speech Recognition in the Arabic Call Domain, by Qusai Abo Obaidah et al.
A New Benchmark for Evaluating Automatic Speech Recognition in the Arabic Call Domain
by Qusai Abo Obaidah, Muhy Eddin Za’ter, Adnan Jaljuli, Ali Mahboub, Asma Hakouz, Bashar Al-Rfooh, Yazan Estaitia
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The abstract proposes a comprehensive benchmark for automatic speech recognition (ASR) in Arabic, tailored to address challenges in telephone conversations. The benchmark aims to emulate real-world conditions by incorporating diverse dialectical expressions and variable call recording quality. This rigorous testing ground is designed to evaluate the performance of ASR systems capable of navigating Arabic speech complexities. State-of-the-art ASR technologies are used as a baseline for evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a comprehensive benchmark for recognizing Arabic speech in phone conversations. It’s hard for machines to understand Arabic because it has many different dialects and sounds that can be tricky to recognize. The researchers want to make sure the benchmark is realistic by including different types of Arabic spoken on phones, as well as varying sound quality. This will help test how well machine learning algorithms can work with real-life Arabic phone conversations. |
Keywords
» Artificial intelligence » Machine learning
