Author: Wang RuiPublisher: Stanford University
ISBN:
Category :
Languages : en
Pages : 99
Book Description
How the human brain processes phonemes has been a subject of interest for linguists and neuroscientists for a long time. Electroencephalography (EEG) offers a promising approach to observe neural activities of phoneme processing in the brain, thanks to its high temporal resolution, low cost and noninvasiveness. The studies on Mismatch Negativity (MMN) effects in EEG activities in the 1990s suggested the existence of a language-specific central phoneme representation in the brain. Recent findings using magnetoencephalograph (MEG) also suggested that the brain encodes the complex acoustic-phonetic information of speech into the representations of phonological features before the lexical information is retrieved. However, very little success has yet been reported in classifying the brain activities associated with phoneme processing. In my work, I proposed a classification framework which incorporates Principal Components Analysis (PCA), cross-validation and support vector machine (SVM) methods. The initial classification rates were not very good. Progress was made by using bootstrap aggregation (Bagging) scheme and introducing phase calculations. To calculate phase, I computed the Discrete Fourier Transform (DFT) of the original time-domain signal and kept the angles of the finite sample of frequencies. The resulting EEG spectral representation contains only the phase and frequency information and ignores the amplitudes. Using this method, the accurate rate of classifying averaged test samples of eight consonants improved from 41% to 51%. Furthermore, the qualitative analysis of the similarities between the EEG representations, derived from the confusion matrices, illustrates the invariance of brain and perceptual representation of phonemes. For brain and perceptual representation of consonants, voicing is the most distinguishable feature among voicing, continuant and place of articulation. And the feature vowel-height is more robust than vowel-backness in both brain and perceptual representation of vowels. By extending and further refining these methods, it is likely significant classification of other phonemes and features can be made.
