Diagnostics for Debugging Speech Recognition Systems

Modern speech recognition applications are becoming very complex program packages. To understand the error behaviour of the ASR systems, a special diagnosis a procedure or a tool is needed. Many ASR users and developers have developed their own expert diagnostic rules that can be successfully applied to a system. There are also several explicit approaches in the literature for determining the problems related to application errors. The approaches are based on error and ablative analyses of the ASR components, with a blame assignment to a problematic component. The disadvantage of those methods is that they are either quite time-consuming to acquire expert diagnostic knowledge, or that they offer very coarse-grained localization of a problematic ASR part. This paper proposes fine-grained diagnostics for debugging ASR by applying a program-spectra based failure localization, and it localizes directly a part of ASR implementation. We designed a toy experiment with diagnostic database OLLO to show that our method is very easy to use and that it provides a good localization accuracy. Because it is not able to localize all the errors, an issue that we discuss in the discussion, we recommend to use it with other coarse-grained localization methods for a complex ASR diagnosis.