PCA Method for Automated Detection of Mispronounced Words
This work addresses improving Computer Assisted Language Learning tools for foreign language learners, presenting an incremental advancement over existing methods like HMMs.
The paper tackles mispronunciation detection for language learners by proposing a PCA-based hierarchical method, achieving up to 99% accuracy in word verification and 93% in native/non-native classification.
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.