AUTOMATIC DETECTION OF PHONE-LEVEL MISPRONUNCIATION 
FOR LANGUAGE LEARNING

Horacio Franco, Leonardo Neumeyer, Marķa Ramos, and Harry Bratt 

SRI International

Speech Technology and Research Laboratory

Menlo Park, CA, 94025, USA

e-mail: {hef,leo,ramos,harry}@speech.sri.com


ABSTRACT

We are interested in automatically detecting specific phone segments
that have been mispronounced by a nonnative student of a foreign
language. The phone-level information allows a language instruction
system to provide the student with feedback about specific pronunciation
mistakes. Two approaches were evaluated; in the first approach,
log-posterior probability-based scores are computed for each phone
segment. These probabilities are based on acoustic models of native
speech. The second approach uses a phonetically labeled nonnative speech
database to train two different acoustic models for each phone: one
model is trained with the acceptable, or correct native-like
pronunciations, while the other model is trained with the incorrect,
strongly nonnative pronunciations. For each phone segment, a
log-likelihood ratio score is computed using the incorrect and correct
pronunciation models.  Either type of score is compared with a phone
dependent threshold to detect a mispronunciation. Performance of both
approaches was evaluated in a phonetically transcribed database of
130,000 phones uttered in continuous speech sentences by 206 nonnative
speakers.