One post tagged with "challenges"

The Clarity Project is based around the idea that machine learning challenges could improve hearing aid signal processing. After all this has happened in other areas, such as automatic speech recognition (ASR) in the presence of noise. The improvements in ASR have happened because of:

• Machine learning (ML) at scale – big data and raw GPU power.
• Benchmarking – research has developed around community-organised evaluations or challenges.
• Collaboration has been enabled by these challenges, allowing working across communities such as signal processing, acoustic modelling, language modelling and machine learning

We’re hoping that these three mechanisms can drive improvements in hearing aids.

Components of a challenge​

There needs to be a common task based on a target application scenario to allow communities to gain from benchmarking and collaboration. Clarity project’s first enhancement challenge will be about hearing speech from a single talker in a typical living room, where there is one source of noise and a little reverberation.