Objective classification of auditory brainstem responses to consonant-vowel syllables using local discriminant bases

• Published in: Speech communication Vol. 114; pp. 36 - 48
• Main Authors: Shirzhiyan, Zahra, Shamsi, Elham, Jafarpisheh, Amir Salar, Jafari, Amir Homayoun
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.11.2019; Elsevier Science Ltd
• Subjects: Algorithms; Classification; Classifiers; Consonants; Consonants (speech); Correlation coefficients; Discriminant analysis; Feature extraction; Frequency domain analysis; Local discriminant bases; Speech ABR; Speech encoding; Support vector machines; Syllables; Time; Vowels; Wavelet analysis; Local discriminant bases; Speech ABR; Speech encoding
• ISSN: 0167-6393; 1872-7182
• DOI: 10.1016/j.specom.2019.09.003

•Classifying the speech ABRs to consonant-vowels objectively.•Finding discriminant features of the responses in three different signal representation domains.•Comparing discriminant features regarding speech-ABRs. The main goal of this study is classifying the auditory brainstem responses to consonant-vowels /da/, /ba/, and /ga/ into three classes automatically. Auditory brainstem responses (ABRs) to consonant-vowel syllables /da/, /ba/, and /ga/ were recorded from twenty-seven normal subjects. Time domain features (i.e., energy, entropy, cosine distance and correlation coefficient) and frequency domain features (i.e., magnitude and phase of the frequency responses) were extracted. Also, local discriminant bases (LDB) method was employed to extract time-frequency features from wavelet packet coefficients. Then, random subset feature selection (RSFS) algorithm reduced the feature spaces. Afterward, discriminant analysis (DA), naive Bayes (NB), multiclass support vector machine (MSVM) and K-nearest neighbors (KNN) methods classified the selected features. The time–frequency domain features showed better results than other features (91.36%). The maximum accuracy was achieved for MSVM classifier when we used a combination of all the features (97.5%). This study shows the efficiency of frequency and time–frequency domains features. The results indicate that time–frequency features obtained by local discriminant bases were more successful in objective classifications of the responses. Besides, the selected features from the phase of the frequency responses were reliable in classifying the responses to consonant-vowels /da/, /ba/ and /ga/. The importance of this study lies in the fact that it helps the objective classification of auditory brainstem responses underlying the different encoding of three consonant-vowels (/ba/, /da/ and /ga/) in frequency and time–frequency domains.