Academic Division: Information Engineering
Research group: Machine Intelligence
Telephone: +44 1223 3 32669
Prof. Woodland’s research is in the area on speech and language technology with a major focus on developing all aspects of speech recognition systems.
His group has developed a number of techniques in that have been widely used in large vocabulary systems including standard methods for transform-based adaptation and discriminative sequence training. He has worked on the use of deep neural networks for both acoustic models and language models. His current work has a focus on the use and development of end-to-end trainable neural network systems. One area of interest is developing flexible systems that can adapt to a wide range of speakers, acoustic conditions, speaking style, language, task etc., with relatively limited training resources. This includes work on unsupervised training, active learning and self-supervised learning, the use of speech and text data for adapting models, as well as contextual speech recognition for biasing neural models. He is also interested in areas including speaker diarisation (who spoke when), emotion recognition from speech data, processing highly overlapped data, multimodal data (speech and video), optimisation techniques large for large sequence-to-sequence models models and confidence estimation.
He is well known for his work on the HTK large vocabulary speech recognition systems.
He has also worked on audio indexing, machine translation from speech, keyword spotting, auditory modelling and speech synthesis.
- MPhil in Machine Learning and Machine Intelligence: Speech Recognition (MLMI2)
- MPhil in Machine Learning and Machine Intelligence: Spoken Language Generation, Processing and Recognition - Advanced Speech Recognition (MLMI14)
- 1st year engineering undergraduates: Mathematics (Easter Term)
- Professorial Fellow of Peterhouse
After working with British Telecom Research Labs for three years, he returned to a Lectureship at the University of Cambridge in 1989 and became a Reader in 1999 and a (full) Professor in 2002. He has authored or couthored more than 250 papers in the area of speech and language technology with a main focus on speech recognition systems. He was the recipient of number of Best Paper awards including for work on speaker adaptation and discriminative training. He is one of the original coauthors of the HTK toolkit and has continued to play a major role in its development. He was a Member of the Editorial Board of Computer Speech and Language (1994–2009) and is currently a Member of the Editorial Board Member of Speech Communication. He is a Fellow of the International Speech Communication Association, the IEEE and the Royal Academy of Engineering.
Recent conferences that have awared the “Best Student Paper” to Prof. Woodland's PhD students as first author have included IEEE Automatic Speech Recognition and Understanding (ASRU) Workshop (2019) and the IEEE Spoken Language Technology (SLT) Workshop (2021) which both have a single best student paper, and the Interspeech 2022 conference (one of 3 awards in different technical areas, from more than 1100 accepted papers).
Department role and responsibilities
- Head of Machine Intelligence Laboratory