Speech and Language Technology in Education (SLaTE 2013)

Grenoble, France
August 30-September 1, 2013

Automatic Pronunciation Feedback for Phonemic Aspiration

Vaishali Patil, Preeti Rao

Department of Electrical Engineering, Indian Institute of Technology Bombay, India

The computer-assisted learning of spoken language is closely tied to automatic speech recognition (ASR) technology which, as is well known, is challenging with non-native speech. By focusing on specific phonological differences between the target and source languages of non-native speakers, pronunciation assessment can be made more reliable. Aspiration, an important phonemic attribute in plosives of Indo-Aryan languages such as Hindi, Marathi and Gujarati, is rarely found in the world's languages. The improper production of the aspiration contrast is thus often the most important cue to non-native accents of spoken Hindi. A system for the detection of phonemic aspiration in unvoiced and voiced stops based on discriminative acoustic features is shown to be effective for rating nonnative accents and providing reliable phoneme-level feedback.

Index Terms: computer-assisted language learning, pronunciation scoring, non-native accent, phonemic aspiration

Full Paper

Bibliographic reference.  Patil, Vaishali / Rao, Preeti (2013): "Automatic pronunciation feedback for phonemic aspiration", In SLaTE-2013, 116-121.