UCL Psychology and Language Sciences


Hearing for Speech

hearing for speech

The major themes of our current research are:
- Speech in noise
- Auditory and phonetic processing in developmental and acquired communication disorders
- Translational research into auditory prostheses
- Normal and impaired auditory perception and cognition

Key researchers:

Andrew Faulkner (Emeritus Professor)

Hearing: especially pitch perception; psychoacoustics of impaired hearing; hearing with a cochlear implant Speech perception: especially perception of degraded and distorted speech; effects of hearing impairment and cochlear implants.

Stuart Rosen

My research is broadly-based in hearing and speech, with an emphasis on the interface between the two. Over the years I have studied a variety of aspects of auditory perception (speech and non-speech) in adults and children, both in disordered and normal populations. I initially came to UCL to work on cochlear implants, and an important part of my current work still concerns this. One strand concerns the optimisation of the transmission of voice melody information in multi-channel implants and a current project for optimal combination of electrical stimulation in one ear with residual hearing in the other (drawing heavily on the expertise developed concerning the auditory processing of people with profound hearing impairment). A separate offshoot of this work concerns the adaptation of cochlear implant users to their new kind of hearing. Incomplete electrode insertion means that patients experience an upward spectral shift in auditory information, and there have been claims that this may be crucial in limiting implant patient performance. Our team was the first to show that people can adapt readily to such spectral shifts, with many further studies both concerning cochlear implants in particular, but also the nature of perceptual adaptation. I spent many years investigating some of the most basic aspects of auditory processing and consideration of the auditory filtering properties of hearing-impaired listeners led our group to the development of a simple description of auditory filtering as a function of level that clarified the nature of the nonlinearity both in normal and impaired hearing. My current interests in this area are more concerned with other decompositions of auditory information, in particular into fine-structure and envelope information. I proposed a classification of the temporal properties of the speech wave more than 15 years ago, and I am still developing various aspects of it. Over the last 10 years, much of my work has shifted focus to more central auditory processes. I am involved in studies of functional brain imaging (PET and fMRI), in attempts to determine the neural substrates for speech and nonspeech processing. I have also been investigating auditory processing in people with specific language impairment and dyslexia, arguing that although auditory processing deficits appear to be more common in such groups, they do not appear to play a causal role in the language deficits Related work concerns the notion of auditory processing disorder and its implications for development.

Meet the researcher:




Emma Holmes

I’m interested in how we percieve sounds in challenging listening environments—such as understanding what a friend's saying when there are other conversations going on around us. In particular, I'm interested in how auditory cognition (e.g., attention and prior knowledge) affects our perception of speech and other sounds, and how these processes are affected by hearing loss. My research combines behavioural techniques (e.g., auditory psychophysics), cognitive neuroscience (e.g., EEG, MEG, and fMRI), and computational modelling.

Josef Schlittenlacher

I'm interested in auditory perception and computational models of the underlying neuroscientific mechanisms and for applied, realistic scenarios. With speech being the most important sound for humans, the biggest part of my research revolves around speech perception. I use and develop deep learning and other machine learning methods to help answer my research questions, as part of computational models and for new applications in the field.