  Phone level localization of mis-articulation is a key requirement for an
automatic articulation error assessment system. A robust phone segmentation
technique is essential to aid in real-time assessment of phone level
mis-articulations of speech, wherein the audio is recorded on mobile phones or
tablets. This is a non-standard recording set-up with little control over the
quality of recording. We propose a novel post processing technique to aid
Spectral Transition Measure(STM)-based phone segmentation under noisy
conditions such as environment noise and clipping, commonly present during a
mobile phone recording. A comparison of the performance of our approach and
phone segmentation using traditional MFCC and PLPCC speech features for
Gaussian noise and clipping is shown. The proposed approach was validated on
TIMIT and Hindi speech corpus and was used to compute phone boundaries for a
set of speech, recorded simultaneously on three devices - a laptop, a
stationarily placed tablet and a handheld mobile phone, to simulate different
audio qualities in a real-time non-standard recording environment. F-ratio was
the metric used to compute the accuracy in phone boundary marking. Experimental
results show an improvement of 7% for TIMIT and 10% for Hindi data over the
baseline approach. Similar results were seen for the set of three of recordings
collected in-house.
