Robust Raw Waveform Speech Recognition Using Relevance Weighted Representations

Speech recognition in noisy and channel distorted scenarios is often challenging as the current acoustic modeling schemes are not adaptive to the changes in the signal distribution in the presence of noise. In this work, we develop a novel acoustic modeling framework for noise robust speech recognition based on relevance weighting mechanism. The relevance weighting is achieved using a sub-network approach that performs feature selection. A relevance sub-network is applied on the output of first layer of a convolutional network model operating on raw speech signals while a second relevance sub-network is applied on the second convolutional layer output. The relevance weights for the first layer correspond to an acoustic filterbank selection while the relevance weights in the second layer perform modulation filter selection. The model is trained for a speech recognition task on noisy and reverberant speech. The speech recognition experiments on multiple datasets (Aurora-4, CHiME-3, VOiCES) reveal that the incorporation of relevance weighting in the neural network architecture improves the speech recognition word error rates significantly (average relative improvements of 10% over the baseline systems)Comment: arXiv admin note: text overlap with arXiv:2001.0706

Robotics Irrigation – A Key to Agricultural Revolution

Agricultural robots are machine programmed to do agricultural task and farm assignment. Despite the large diffusion of robotic and automated solutions that took place during the last decades in most production processes, the agricultural sector benefited only marginally from automated solutions. Most of the farming is now done with machines but they are not automated, hence there is a need of another revolution in agriculture and that is robotics and automation revolution.Agricultural robots can be classified into several groups: harvesting or picking, planting, weeding, pest control, maintenance or irrigation. Out of these, irrigation robots have been researched and implemented very less but are of a great importance to increase the production of a crop. Different type of crops has different types of irrigation requirements and should be dealt accordingly. This can be efficiently done if robotics is integrated in irrigation. Hereby, in this paper, we are proposing efficient ways of irrigation by robots, their advantages and future perspectives. Our approach is to utilize available information technologies and the proposed framework in the form of more intelligent machines to reduce and target energy inputs in more effective ways than in the past

Effect of early maternal newborn skin to skin contact in labour room on third stage of labour and success at breastfeeding

Background: Immediate postpartum period and birth pose many challenges for the mother and the new-born. Initiation of early skin to skin contact in the labour room can be beneficial to both of them.Methods: Randomized control trial conducted over a period of 7 months in a tertiary care centre enrolling 400 laboring women.200 in the control group were given routine care. In the 200women in the study group, the newborn was given immediate skin to skin contact by placing him/her on the mother’s chest.Results: Duration of third stage of labour was less than 10 minutes in 95%women of study group compared to 56% women in the control group(p<0.01). Placenta was expulsed as a whole in 98% cases in the study group compared to 81% in the control group. Successful breastfeeding was observed in 88% women in study group compared to 54%in the control group(p<0.01). Breastfeeding was initiated within 30 minutes of birth in 96%women in the study group compared to 41% in the control group.Conclusions: Uterus could contract faster with the complete expulsion of placenta and shortening of the third stage of labour with early skin to skin contact. The newborn showed early initiation, success at breastfeeding and longer first breastfeeding with early skin to skin contact

Unsupervised modulation filter learning for noise-robust speech recognition

The modulation filtering approach to robust automatic speech recognition (ASR) is based on enhancing perceptually relevant regions of the modulation spectrum while suppressing the regions susceptible to noise. In this paper, a data- driven unsupervised modulation filter learning scheme is proposed using convolutional restricted Boltzmann machine. The initial filter is learned using the speech spectrogram while subsequent filters are learned using residual spectrograms. The modulation filtered spectrograms are used for ASR experiments on noisy and reverberant speech where these features provide significant improvements over other robust features. Furthermore, the application of the proposed method for semi- supervised learning is investigated. (C) 2017 Acoustical Society of America

Molecular features of steroid-binding antidins and their use for assaying serum progesterone

Chicken avidin (Avd) and streptavidin from Streptomyces avidinii are extensively used in bionanotechnology due to their extremely tight binding to biotin (Kd ~ 10-15 M for chicken Avd). We previously reported engineered Avds known as antidins, which have micro- to nanomolar affinities for steroids, non-natural ligands of Avd. Here, we report the 2.8 Å X-ray structure of the sbAvd-2 (I117Y) antidin co-crystallized with progesterone. We describe the creation of new synthetic phage display libraries and report the experimental as well as computational binding analysis of progesterone-binding antidins. We introduce a next-generation antidin with 5 nM binding affinity for progesterone, and demonstrate the use of antidins for measuring progesterone in serum samples. Our data give insights on how to engineer and alter the binding preferences of Avds and to develop better molecular tools for modern bionanotechnological applications