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license: mit |
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ABX-accent |
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The ABX-accent project is based on the preparation and evaluation of the Accented English Speech Recognition Challenge (AESRC) dataset [1], using fastABX [2] for evaluation. This repository provides all the items files you can use for evaluation. |
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What is ABX Evaluation? |
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The ABX metric evaluates whether a representation X of a speech unit (e.g., the triphone “bap”) is closer to a same-category example A (also “bap”) than to a different-category example B (e.g., “bop”). The ABX error rate is calculated by averaging the discrimination errors over all minimal triphone pairs (ie., differing only by the central phoneme) in the corpus. |
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This benchmark focuses on the more challenging ABX across speaker task, where the X example is spoken by a different speaker than the ones in pair (A, B), testing speaker-invariant phonetic discrimination. |
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This benchmark focuses on the more challenging ABX across speaker task, where the X example is spoken by a different speaker than the ones in pair (A, B), testing speaker-invariant phonetic discrimination. |
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About the Dataset. |
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The **[Accented English Speech Recognition Challenge](https://arxiv.org/abs/2102.10233)** dataset includes recordings from ten different regional accents: |
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American, British, Canadian, Chinese, Indian, Japanese, Korean, Portuguese, Spanish, Russian. |
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For academic research only. You can apply this dataset following the instructions on this page: https://www.nexdata.ai/company/sponsored-datasets. |
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Getting Started |
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To begin working with the AESRC development data and run evaluations, you will find the following resources in the [GitHub repository](https://github.com/bootphon/ABX-accent). |
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The benchmark is part of the first task of the ZeroSpeech Benchmark on https://zerospeech.com. |
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References |
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- [1] Xian Shi, Fan Yu, Yizhou Lu, Yuhao Liang, Qiangze Feng, Daliang Wang, Yanmin Qian, and Lei Xie, “The accented english speech recognition challenge 2020: open datasets, tracks, baselines, results and methods,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).IEEE, 2021, pp. 6918–6922. |
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- [2] Maxime Poli, Emmanuel Chemla, Emmanuel Dupoux "fastabx: A library for efficient computation of ABX discriminability" arXiv:2505.02692v1 [cs.CL] 5 May 2025. |
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