Automatic Extraction of Rich Metadata from Broadcast Speech

从广播语音中自动提取丰富的元数据

• 批准号: 2104504
• 金额: --
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2104504
• 关键词: Automatic; Extraction; Rich; Metadata; Broadcast

The research studentship will be concerned with automatically learning to extract rich metadata information from broadcast television recordings, using speech recognition and natural language processing techniques. We will build on recent advances in convolutional and recurrent neural networks, using architectures which learn representations jointly, considering both acoustic and textual data. The project will build on our current work in the rich transcription of broadcast speech using neural network based speech recognition systems, along with neural network approaches to machine reading and summarisation. In particular, we are interested in developing approaches to transcribing broadcast speech in a way appropriate to the particular context. This may include compression or distillation of the content (perhaps to fit in with the constraints of subtitling), transforming conversational speech into a form that is more easy to read as text, or transcribing broadcast speech in a way appropriate for a particular reading age.

研究生将关注自动学习从广播电视录音中提取丰富的元数据信息，使用语音识别和自然语言处理技术。我们将基于卷积和递归神经网络的最新进展，使用联合学习表示的架构，同时考虑声学和文本数据。该项目将建立在我们目前的工作，在丰富的转录广播语音使用神经网络为基础的语音识别系统，沿着神经网络的方法来机器阅读和总结。特别是，我们感兴趣的是开发方法，以适当的方式转录广播讲话的特定背景。这可能包括压缩或提炼内容（也许是为了适应字幕的限制），将对话语音转换为更容易阅读的文本形式，或者以适合特定阅读年龄的方式转录广播语音。