While the performance of ASR and parsing components are often optimized independently of each other, in particular in case of the ASR to minimize recognitions errors, research has shown that ASR transcriptions with a lower error rate can in fact yield worse understanding performance Bayer and Riccardi, 2012) and that joint approaches to recognition and understanding can yield improved performance (Wang and Acero, 2006b;Deoras et al., 2013).
wang and Acero (2006b) have shown that applying the same grammar for speech recognition and understanding can yield improved understanding performance compared to applying a standard n-gram model with the ASR, since dependencies between acoustics and semantics can be captured.