Automatic Extraction of Rich Metadata from Broadcast Speech

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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.