  This paper presents a method for detecting mispronunciations with the aim of
improving Computer Assisted Language Learning (CALL) tools used by foreign
language learners. The algorithm is based on Principle Component Analysis
(PCA). It is hierarchical with each successive step refining the estimate to
classify the test word as being either mispronounced or correct. Preprocessing
before detection, like normalization and time-scale modification, is
implemented to guarantee uniformity of the feature vectors input to the
detection system. The performance using various features including spectrograms
and Mel-Frequency Cepstral Coefficients (MFCCs) are compared and evaluated.
Best results were obtained using MFCCs, achieving up to 99% accuracy in word
verification and 93% in native/non-native classification. Compared with Hidden
Markov Models (HMMs) which are used pervasively in recognition application,
this particular approach is computational efficient and effective when training
data is limited.
