Movements of articulators (e.g., tongue, lips and jaw) in different speaking rates are related in a complex manner. In this work, we examine the underlying function to transform articulatory movements involved in producing speech at a neutral speaking rate into those at fast and slow speaking rates (N2F and N2S). For this we use articulatory movement data collected from five subjects using an Electromagnetic articulograph at neutral, fast and slow speaking rates. As candidate transformation functions (TF), we use affine transformations with a diagonal matrix and a full matrix and a nonlinear function modeled by a deep neural network (DNN). Since the duration of an utterance in different speaking rates would typically be unequal, it is required to time align the articulatory movement trajectories, which, in turn, affects the TF learnt. Therefore, we propose an iterative algorithm to alternately optimize for the TF and the time alignments. Subject specific experiments reveal that while N2F transformation can be well described by an affine transformation with a full matrix, N2S transformation is better represented by a more complex nonlinear function modeled by a DNN. This could be because subjects exhibit gross articulatory movements during fast speech and hyper-articulate while producing slow speech.

We propose a bidirectional long short-term memory (BLSTM) based whispered speech to neutral speech conversion system that employs the STRAIGHT speech synthesizer. We use a BLSTM to map the spectral features of whispered speech to those of neutral speech. Three other BLSTMs are employed to predict the pitch, periodicity levels and the voiced/unvoiced phoneme decisions from the spectral features of whispered speech. We use objective measures to quantify the quality of the predicted spectral features and excitation parameters, using data recorded from six subjects, in a four fold setup. We find that the temporal smoothness of the spectral features predicted using the proposed BLSTM based system is statistically more compared to that predicted using deep neural network based baseline schemes. We also observe that while the performance of the proposed system is comparable to the baseline scheme for pitch prediction, it is superior in terms of classifying voicing decisions and predicting periodicity levels. From subjective evaluation via listening test, we find that the proposed method is chosen as the best performing scheme 26.61% (absolute) more often than the best baseline scheme. This reveals that the proposed method yields a more natural sounding neutral speech from whispered speech.

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In this work an attempt is made to analysis of the possible different conformers of p-coumaric acid (PCA) by using density functional method. The total energy of four possible conformers were calculated by using B3LYP/6-311G(d,p) method. Computational result identifies that the most stable conformer of PCA is C2. The formation of inter-and intra-molecular hydrogen bonding between -OH and -COOH group gave the evidence for dimer formation for PCA molecule. The highest occupied-lowest unoccupied molecular orbital analysis shows that the negative electrostatic region situated over the -COOH group and positive electrostatic potential region are localized on ring system and all hydrogen. The PCA has been screened to anti-microbial activity and found to exhibit antibacterial effects. Molecular docking results suggest that PCA may exhibit inhibitory activity against lung cancer protein and may act as potential against lung cancer.

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