COST278 and ISCA Tutorial and Research Workshop (ITRW) on Robustness Issues in Conversational Interaction
University of East Anglia, Norwich, UK
Nowadays, automatic speech recognizers have become quite good in recognizing well prepared fluent speech (e.g. news readings). However, the recognition of spontaneous speech is still problematic. Some reasons for this are that spontaneous speech is usually less articulated and that it can contain a lot of disfluencies such as filled pauses (FPs), abbreviatons, repetitions, etc. In this paper, a new methodology for coping with FPs is presented. The basic idea is to detect FPs, and let this information control/modify the search for word hypotheses. Just counting normal words (excluding FPs), we can presently eliminate about one word error per FP occurring in the speech, and this without introducing a significant augmentation of the computational load.
Bibliographic reference. Stouten, Frederik / Martens, Jean-Pierre (2004): "Benefits of disfluency detection in spontaneous speech recognition", In Robust2004, paper 20.