- Computational modelling of spoken language processing
- Application of speech technology
- Human - robot interaction
I have over forty years experience in speech technology R&D, and much of my research has been based on insights derived from human speech perception and production. I built my first automatic speech recogniser – EARS (Electronic Apparatus for Recognising Speech) in 1972, and in the mid-1970s I introduced the Human Equivalent Noise Ratio (HENR) – a vocabulary-independent measure of the goodness of an automatic speech recogniser based on a computational model of human word recognition. In the 1980s, I published HMM Decomposition – a powerful method for recognising multiple simultaneous signals (such as speech in noise) based on observed properties of the human auditory system. During the 1990s and more recently, I’ve continued to champion the need to understand the similarities and differences between human and machine spoken language behaviour.
Since joining Sheffield I’ve embarked on research that is aimed at developing computational models of Spoken Language Processing by Mind and Machine, and I’m currently working on a unified theory of spoken language processing called PRESENCE (PREdictive SENsorimotor Control and Emulation). PRESENCE weaves together accounts from a wide variety of different disciplines concerned with the behaviour of living systems – many of them outside the normal realms of spoken language – and compiles them into a new framework that is intended to breath life into a new generation of research into spoken language processing, especially for Autonomous Social Agentsand Human-Robot Interaction.
At the more practical end of the scale, I’m involved in collaborations aimed at Clinical Applications of Speech Technology (particularly for individuals with speaking difficulties) and I’m becoming increasingly involved in Creative Applications of Speech Technologythrough interactions with colleagues from the performing arts.