|Fabrizio Morbini, Kartik Audhkhasi, Kenji Sagae, Ron Artstein, Dogan Can, Panayiotis Georgiou, Shri Narayanan, Anton Leuski, David Traum|
We present an analysis of several publicly available automatic speech recognizers (ASRs) in terms of their suitability for use in different types of dialogue systems. We focus in particular on cloud based ASRs that recently have become available to the community. We include features of ASR systems and desiderata and requirements for different dialogue systems, taking into account the dialogue genre, type of user, and other features. We then present speech recognition results for six different dialogue systems. The most interesting result is that different ASR systems perform best on the data sets. We also show that there is an improvement over a previous generation of recognizers on some of these data sets. We also investigate language understanding (NLU) on the ASR output, and explore the relationship between ASR and NLU performance.