Advanced audio noise characterization and filtration for accelerated Automated Speech Recognition

Collaborators

Researcher

Environmental noise can provide a rich source of information about the current context. For example, humans often infer the location of a respondent in a mobile phone conversation by identifying the background noise and adjusting their responses accordingly. Emulating this capability using computers and automated algorithms is a challenging problem due to the inherent diversity and complexity of background noise. The need for characterising noise is increasingly in demand due to its applicability in a number of applications including speech recognition and voice activity detection. This project aims to develop a set of novel tools that will facilitate the automatic characterisation of the surrounding noise-field via blind audio recordings from a single/ multiple microphones, and analyse its applicability in advancing existing speech recognition techniques. The specific objectives of this project are: (i) Development and evaluation of an environmental noise classifier using an advanced feature vector; (ii) Robust speech detection directed by the aforementioned noise classifier.

 

Funding

This is an Industry Project funded by the Australian Signals Directorate as part of the ANU-ASD CoLAB.

 

Partners

Australian Signals Directorate

Updated:  1 June 2019/Responsible Officer:  Dean, CECS/Page Contact:  CECS Marketing