  In this paper, we have introduced and evaluated intonation based feature for
scoring the English speech of nonnative English speakers in Indian context. For
this, we created an automated spoken English scoring engine to learn from the
manual evaluation of spoken English. This involved using an existing Automatic
Speech Recognition (ASR) engine to convert the speech to text. Thereafter,
macro features like accuracy, fluency and prosodic features were used to build
a scoring model. In the process, we introduced SimIntonation, short for
similarity between spoken intonation pattern and "ideal" i.e. training
intonation pattern. Our results show that it is a highly predictive feature
under controlled environment. We also categorized interword pauses into 4
distinct types for a granular evaluation of pauses and their impact on speech
evaluation. Moreover, we took steps to moderate test difficulty through its
evaluation across parameters like difficult word count, average sentence
readability and lexical density. Our results show that macro features like
accuracy and intonation, and micro features like pause-topography are strongly
predictive. The scoring of spoken English is not within the purview of this
paper.
