About
One of the greatest challenges for hearing-impaired listeners is understanding speech in the presence of background noise. Noise levels encountered in everyday social situations can have a devastating impact on speech intelligibility, and thus communication effectiveness, potentially leading to social withdrawal and isolation. Disabling hearing impairment affects 360 million people worldwide, with that number increasing because of the ageing population. Unfortunately, current hearing aid technology is often ineffective at restoring speech intelligibility in noisy situations. .
To allow the development of better hearing aids, we need better ways to evaluate the speech intelligibility of audio signals. We need prediction models that can take audio signals and use knowledge of the listener's characteristics (e.g., an audiogram) to estimate the signal’s intelligibility. Further, we need models that can estimate intelligibility not just of natural signals, but also of signals that have been processed using hearing aid algorithms - whether current or under development. The need for such models has been highlighted by the recent Clarity Enhancement Challenge (CEC), in which 11 teams worldwide built systems to enhance speech-in-noise scenes for evaluation by hearing-impaired listeners. Analysis of the listening test results has shown that currently-used intelligibility metrics are not able to predict individual's speech-in-noise performance at a sentence-by-sentence level. It is this problem that sets the scene for this special session.
The Clarity Prediction Challenge
As a focus for the session, we have launched the Clarity Prediction Challenge (CPC). The challenge provides you with noisy speech signals that have been processed with a number of hearing aid signal processing systems and corresponding intelligibility scores produced by a panel of hearing-impaired individuals. You are tasked with producing a model that can predict intelligibility scores given just the signals, their clean references and a characterisation of each listener’s specific hearing impairment. The challenge will remain open until the Interspeech submission deadline and all entrants are welcome. (Note, the Clarity Prediction Challenge is part of a 5-year programme with further prediction and enhancement challenges planned for the future.)
Topics
The session welcomes submission from entrants to the Clarity Prediction Challenge but is also inviting papers on related to topics in hearing impairment and speech intelligibility, including, but not limited to,
- Statistical speech modelling for intelligibility prediction
- Modelling energetic and informational noise masking
- Individualising intelligibility models using audiometric data
- Intelligibility prediction in online and low latency settings
- Model-driven speech intelligibility enhancement
- New methodologies for intelligibility model evaluation
- Speech resources for intelligibility model evaluation
- Applications of intelligibility modelling in acoustic engineering
- Modelling interactions between hearing impairment and speaking style
- Papers using the data supplied with the Clarity Prediction Challenge
Important Dates
- Submission: March 21st initial (March 28th finalised)
- Acceptance notification: June 13th
- Camera-ready paper: June 23rd
- Conference dates: September 18th-22nd
Paper Submission
Papers for this special session have to be submitted following the same schedule and procedure as regular Interspeech papers; the papers will undergo the same review process by anonymous and independent reviewers.
Please submit your paper to the special session "Speech Intelligibility for Hearing Impaired Listeners".
For participation in the Clarity Prediction Challenge please also follow the system submission instructions available on the CPC website.
Organizers
If you have any questions regarding the session please don't hesitate to contact one of the organizers listed below,
- Trevor Cox, University of Salford, UK
- Fei Chen, Southern University of Science and Technology, China
- Jon Barker, University of Sheffield, UK
- Daniel Korzekwa, Amazon TTS
- Michael Akeroyd, University of Nottingham, UK
- John Culling, University of Cardiff, UK
- Graham Naylor, University of Nottingham, UK