CI-EN: Enhancements for the Kaldi Speech Recognition Toolkit

自动语音识别（ASR）技术的社会效益开始累积，这要归功于创造性的新设备和应用，例如移动的设备上的语音搜索、通过语音命令实现的车载功能以及用于语言学习的教育软件。 然而，应用程序开发人员很难掌握ASR技术的复杂性。 使用ASR的学术研究，例如用于研究人机对话或儿童语言习得，也有类似的重大进入障碍。 最后，ASR研究人员本身需要最先进的基线系统来进行进一步的改进，这对于大多数学术界甚至一些行业团体来说都是一项艰巨的任务。 因此，对免费提供、易于使用、最先进并与ASR技术的新进展保持同步的ASR工具包的需求非常强烈。 Kaldi语音识别工具包的开发部分得到了过去NSF奖项的支持，它（i）可通过http://kaldi.sourceforge.net免费获得，（ii）提供易于使用的配方，为许多广泛使用的数据集开发高性能的ASR系统，（iii）被数百名研究人员采用，以满足上述需求。 因此，保持Kaldi的最新状态，并为Kaldi用户提供建议和技术支持，正成为各种学科和工业部门的教师，学生和开发人员研究的关键推动因素。CISE研究基础设施项目旨在增强和维护Kaldi语音识别工具包。 首先创建Kaldi的约翰霍普金斯大学的研究人员将进一步开发其功能，以更好地支持未来的研究，无论是在核心ASR还是ASR应用中。 他们最近增加了对深度神经网络训练和在线（实时）解码的支持。 Kaldi将开发和添加的具体新功能包括改进的语音活动检测，更快的解码器，对递归神经网络语言模型的支持，更灵活的深度神经网络实验框架，以及递归神经网络声学模型等相关功能。 研究人员将通过Kaldi用户经常参加的科学会议和研讨会公布他们的专业知识并传播这些增强功能，并将通过讨论论坛和会议参与不断征求他们对未来增强功能的反馈。 他们还建立了一个邮件列表和一个讨论论坛，供Kaldi用户发布技术问题和交流常见问题的解决方案。 该项目还将通过这些在线讨论列表和开发人员论坛提供用户支持。

Societal benefits of automatic speech recognition (ASR) technology are beginning to accrue, thanks to creative new devices and applications, such as voice-enabled search on mobile devices, in-car functions via voice commands, and educational software for language learning. Application developers, however, have a difficult time mastering the complexities of ASR technology. Academic research who use ASR, e.g. for studying human-computer dialog or language acquisition by children, also have a similarly significant barrier to entry. Finally, ASR researchers themselves need state-of-the-art baseline systems on which to make further improvements, which is a daunting task for most academic and even some industry groups. There is thus a strong demand for an ASR toolkit that is freely available, easy to use, state-of-the-art, and kept up to date with new advances in ASR technology. The Kaldi speech recognition toolkit, whose development was partially supported by past NSF awards is (i) freely available via http://kaldi.sourceforge.net, (ii) provides easy-to-use recipes to develop high-performing ASR systems for a number of widely used datasets, and (iii) is being adopted by hundreds of researchers to fulfill the aforementioned needs. Keeping Kaldi up-to-date and providing advice and technical support to Kaldi users is therefore becoming a crucial enabler of the research of faculty, students and developers in a variety of academic disciplines and industrial sectors.This CISE research infrastructure project seeks to enhance and maintain the Kaldi speech recognition toolkit. Johns Hopkins University researchers, who first created Kaldi, will further develop its capabilities to better enable future research, both in core ASR and ASR applications. They have recently added support for deep neural network training and online (real-time) decoding. Specific new features that will be developed and added to Kaldi include improved voice activity detection, a faster decoder, support for recurrent neural net language models, a more flexible framework for experimenting with deep neural networks, and related capabilities such as recurrent neural net acoustic models. The investigators will publish their know-how and disseminate these enhancements through scientific conferences and workshops frequently attended by Kaldi users, and will continually solicit their feedback about future enhancements through discussion forums and conference participation. They have also already set up a mailing list and a discussion forum for Kaldi users to post technical questions and exchange solutions to commonly encountered problems. User support will also be provided by this project via these on-line discussion lists and developers forums.