Automatic Extraction of Rich Metadata from Broadcast Speech

该研究项目将致力于利用语音识别和自然语言处理技术，自动学习从广播电视录像中提取丰富的元数据信息。我们将在卷积和递归神经网络的最新进展的基础上，使用联合学习表示法的体系结构，考虑声学和文本数据。该项目将建立在我们目前的工作基础上，利用基于神经网络的语音识别系统，以及机器阅读和摘要的神经网络方法，丰富广播语音的转录。特别是，我们感兴趣的是开发以适合特定背景的方式转录广播演讲的方法。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易于阅读的文本形式，或者以适合特定阅读年龄的方式转录广播演讲。

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.