对于低资源的刚果语语言,语音识别数据集
Ussen Kimanuka1, Ciira Wa Maina2,3, Osman Büyük4
1Department of Electrical Engineering, Pan African University Institute for Basic Sciences, Technology and Innovation, Nairobi, Kenya.
Data in brief
|December 11, 2023
概括
为林加拉语和其他刚果语言创建了新的语音识别数据集. 这些资源有助于为低资源语言开发先进的自动语音识别 (ASR) 模型.
科学领域:
- 计算语言学 计算语言学
- 语音处理 语音处理
- 低资源的语言技术
背景情况:
- 大型预训练自动语音识别 (ASR) 模型从转移学习中受益,但需要大量的数据.
- 许多低资源语言缺乏足够的数据来充分利用转移学习.
- 基准体对于在数据稀缺的语言环境中推进ASR方法至关重要.
研究的目的:
- 为刚果民主共和国的低资源语言引入两个新的基准语料库.
- 促进对刚果语言的单语和多语ASR系统的开发.
- 为了使林加拉语和其他四种刚果语言的语音识别系统能够进行首次基准测试.
主要方法:
- 创建了林加拉朗读语音库 (4小时标记音频),拥有多样化的扬声器和口音.
- 来自广播档案的刚果语音广播集体 (741小时未标记的音频) 的汇编.
- 监督学习和自我监督学习技术的应用,用于模型开发和基准测试.
主要成果:
- 开发的公司为ASR研究在资源较少的环境中提供了宝贵的资源.
- 成功的首次对林加拉语语音识别系统进行基准测试.
- 为四种刚果语言开发第一个多语言的ASR模型,为9500万人口提供服务.
结论:
- 发布的数据集对于推进服务不足的语言的ASR研究和开发至关重要.
- 这些资源为刚果民主共和国改进语音识别技术铺平了道路.
- 该研究强调了转移学习和新型机构对低资源ASR的潜力.
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