Various algorithms for text-independent speaker recognition have been developed through the decades, aiming
to improve both accuracy and efficiency. This paper presents a novel PCA/LDA-based approach that is faster
than traditional statistical model-based methods and achieves competitive results. First, the performance based
on only PCA and only LDA is measured; then a mixed model, taking advantages of both methods, is introduced.
A subset of the TIMIT corpus composed of 200 male speakers, is used for enrollment, validation and testing.
The best results achieve 100%, 96% and 95% classification rate at population level 50, 100 and 200, using 39-
dimensional MFCC features with delta and double delta. These results are based on 12-second text-independent
speech for training and 4-second data for test. These are comparable to the conventional MFCC-GMM methods,
but require significantly less time to train and operate.
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.
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