Overview

CHiME aims to build a speech recogniser that can operate reliably in everyday `acoustically cluttered' environment. The research will build on an existing framework know as speech fragment decoding. This technique, inspired by the scene-analysis account of auditory perception, operates in two stages: first, signal processing techniques to split the acoustic mixture into local time-frequency fragments of individual sound sources; second, statistical models are employed to select fragments belonging to the sound source of interest while rejecting fragments coming from distracting sound sources.

Objectives

The project has outline a number of key objective which will extend the fragment decoding framework in directions needed to bridge the gap between theory and real applications:

• Segmentation models: modelling the processes that track signal properties (e.g. location and pitch) across time and frequency to group isolated sound source fragments.
• Model combination: techniques for describing complex acoustic scenes by combining models of individual sources.
• Efficient search: how can multiple models be combined with combinatorial explosion of the search space?
• Adaptation: developing always-on systems that learn as they listen.
• Demonstration: deploying fragment decoding in a real-time distant-microphone speech-driven interface.

Evaluation

The CHiME speech recognition systems will be evaluated on a recognition task that simulates a speech-driven home automation system. Binaural (i.e. stereo) audio data is being recorded in a number of noisy domestic spaces (living rooms, dining rooms, kitchen). By using impulse responses carefully recorded in the same rooms, a standard speech recognition evaluation corpus (the Grid corpus) will be mixed into the data as though it had been recorded in the rooms themselves. This will allow the construction of a corpus of realistic and yet carefully controlled noisy utterances, the CHiME corpus.

The data will form the basis of an open ASR competition (the CHiME challenge) that will allow the CHiME approach to be compared with external competing systems. Details of the challenge will be announced later in 2010.