现实和合成的旁遮普语语音数据集用于自动语音识别
Satwinder Singh1, Feng Hou1, Ruili Wang1
1School of Mathematical and Computational Sciences, Massey University, Auckland, New Zealand.
Data in brief
|December 26, 2023
概括
研究人员开发了三个新的旁遮普语语音数据集,以改善低资源语言的自动语音识别 (ASR). 这项工作解决了对旁遮普语ASR系统缺乏高质量的注释数据的问题.
科学领域:
- 计算语言学 计算语言学
- 语音处理 语音处理
背景情况:
- 自动语音识别 (ASR) 系统需要大量的注释数据集以获得强大的性能.
- 像英语这样的高资源语言主导着可用的ASR数据集,为低资源语言创造了资源差距.
- 旁遮普语,尽管有众多的发言者,但缺乏足够高质量的注释语音数据集.
研究的目的:
- 为了解决标记的旁遮普语语音数据的稀缺问题.
- 促进开发精确的旁遮普语自动语音识别系统.
- 为了弥合低资源语言的资源差距,ASR.
主要方法:
- 介绍了三个新的旁遮普语语音数据集:旁遮普语音 (真实录音),谷歌合成 (谷歌TTS) 和CMU合成 (集群基因模型).
- 孟加拉语语音数据集包括在不同环境 (工作室和开放环境) 中记录的读音语音.
- 在节日系统中使用谷歌的Punjabi文字转换和CMU的Clustergen模型创建的合成数据集.
主要成果:
- 创建了三个不同的,标记的旁遮普语语音数据集.
- 现实和合成语音数据的可用性,用于ASR模型训练.
- 建立资源,以支持研究在旁遮普语语音识别.
结论:
- 新推出的数据集为推进Punjabi ASR提供了至关重要的资源.
- 这些数据集将使更准确和更强大的旁遮普语语音识别模型的培训成为可能.
- 这一举措有助于缓解在ASR领域对Punjabi的数据短缺问题.
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