Exploring different acoustic modeling techniques for the detection of vowels in speech signal

In this paper, we explore acoustic modeling techniques based on the Gaussian mixture modeling (GMM), the subspace GMM (SGMM) and deep neural network (DNN) for the detection of vowels in a given speech signal. At the outset, we develop a recognition system on the TIMIT database that recognizes the sequence of phonetic units present in a given speech sample. Two recognizers are developed using speech data sampled at 16 kHz and 8 kHz rates, respectively. The phone error rates (classification errors) for the two recognizers help in studying the effect of sampling rate on the classifier performance. The experimental evaluations presented in this study show that there is a slight deterioration in the recognition performance when speech data is re-sampled to 8 kHz rate. Next, a three-class classifier (vowel, non-vowel and silence) is also developed on the TIMIT database and the classification performances are studied. Using the three-class classifier, a given speech sample is then forced aligned against the trained acoustic model under the constraints of true/first-pass transcriptions to detect the vowel regions. The correctly detected and spurious vowel regions are analyzed in detail to find the impact of semivowel and nasal sound units on the detection of vowel regions as well as on the determination of vowel onset and end points. Among the explored acoustic modeling techniques, the SGMM-based system is observed to superior to all other systems. Furthermore, for all the studied modeling techniques, the spurious cases are mostly due to the detection of semivowels as the vowels.