Articulatory features for "meeting" speech recognition
Florian Metze · 2006
“Meeting ” speech, for example from the RT-04S task, contains a mixture of different speaking styles that leads to word error rates higher than 25 % even when close-talking microphones are being used. The problem is even more serious, as word error rates are particularly high when speakers use a clear speaking mode, for example because they want to stress an important point. Previ-ous work showed that an approach that combines standard phone-based acoustic models with models detecting the presence or ab-sence of “Articulatory Features ” such as “Rounded ” or “Voiced” can improve ASR performance particularly for these cases. This paper presents a discriminative approach to automatically comput-ing from training or adaptation data the feature stream weights needed for the above approach, therefore presenting a framework for integrating articulatory features into existing automatic speech recognition systems. We find a 7 % relative improvements on top of our best RT-04S system using discriminative adaptation. 1.