Mukesh Sharma

Abhishek Niranjan, Mukesh Sharma, Sai Bharath Chandra Gutha et al.

Machine recognition of an atypical speech like whispered speech, is a challenging task. We introduce whisper-to-natural-speech conversion using sequence-to-sequence approach by proposing enhanced transformer architecture, which uses both parallel and non-parallel data. We investigate different features like Mel frequency cepstral coefficients and smoothed spectral features. The proposed networks are trained end-to-end using supervised approach for feature-to-feature transformation. Further, we also investigate the effectiveness of embedded auxillary decoder used after N encoder sub-layers, trained with the frame-level objective function for identifying source phoneme labels. We show results on opensource wTIMIT and CHAINS datasets by measuring word error rate using end-to-end ASR and also BLEU scores for the generated speech. Alternatively, we also propose a novel method to measure spectral shape of it by measuring formant distributions w.r.t. reference speech, as formant divergence metric. We have found whisper-to-natural converted speech formants probability distribution is similar to the groundtruth distribution. To the authors' best knowledge, this is the first time enhanced transformer has been proposed, both with and without auxiliary decoder for whisper-to-natural-speech conversion and vice versa.

Debarati B. Chakraborty, Mukesh Sharma, Bhaskar Vijay

A novel concept of vision-based intelligent control of robotic arms is developed here in this work. This work enables the controlling of robotic arms motion only with visual inputs, that is, controlling by showing the videos of correct movements. This work can broadly be sub-divided into two segments. The first part of this work is to develop an unsupervised vision-based method to control robotic arm in 2-D plane, and the second one is with deep CNN in the same task in 3-D plane. The first method is unsupervised, where our aim is to perform mimicking of human arm motion in real-time by a manipulator. We developed a network, namely the vision-to-motion optical network (DON), where the input should be a video stream containing hand movements of human, the the output would be out the velocity and torque information of the hand movements shown in the videos. The output information of the DON is then fed to the robotic arm by enabling it to generate motion according to the real hand videos. The method has been tested with both live-stream video feed as well as on recorded video obtained from a monocular camera even by intelligently predicting the trajectory of human hand hand when it gets occluded. This is why the mimicry of the arm incorporates some intelligence to it and becomes intelligent mimic (i-mimic). Alongside the unsupervised method another method has also been developed deploying the deep neural network technique with CNN (Convolutional Neural Network) to perform the mimicking, where labelled datasets are used for training. The same dataset, as used in the unsupervised DON-based method, is used in the deep CNN method, after manual annotations. Both the proposed methods are validated with off-line as well as with on-line video datasets in real-time. The entire methodology is validated with real-time 1-link and simulated n-link manipulators alongwith suitable comparisons.