首页> 外文期刊>Journal of Computers >Enhancements in Statistical Spoken Language Translation by De-normalization of ASR Results
【24h】

Enhancements in Statistical Spoken Language Translation by De-normalization of ASR Results

机译:通过ASR结果的转发,统计口语翻译中的增强

获取原文
           

摘要

—Spoken language translation (SLT) has become very important in an increasingly globalized world. Machine translation (MT) for automatic speech recognition (ASR) systems is a major challenge of great interest. This research investigates that automatic sentence segmentation of speech that is important for enriching speech recognition output and for aiding downstream language processing. This article focuses on the automatic sentence segmentation of speech and improving MT results. We explore the problem of identifying sentence boundaries in the transcriptions produced by automatic speech recognition systems in the Polish language. We also experiment with reverse normalization of the recognized speech samples.
机译:- 在越来越全球化的世界中, - 翻译(SLT)已经变得非常重要。用于自动语音识别(ASR)系统的机器翻译(MT)是极大兴趣的主要挑战。本研究调查了语音的自动句子分割,这对于丰富语音识别输出和解除下游语言处理很重要。本文重点介绍了言语的自动句子分割,提高了MT结果。我们探讨了在波兰语中自动语音识别系统产生的转录中识别句子边界的问题。我们还试验了认可的语音样本的反向正常化。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号