A study on different linear and non-linear filtering techniques of speech and speech recognition

In any signal noise is an undesired quantity, however most of thetime every signal get mixed with noise at different levels of theirprocessing and application, due to which the information containedby the signal gets distorted and makes the whole signal redundant.A speech signal is very prominent with acoustical noises like bubblenoise, car noise, street noise etc. So for removing the noises researchershave developed various techniques which are called filtering. Basicallyall the filtering techniques are not suitable for every application,hence based on the type of application some techniques are betterthan the others. Broadly, the filtering techniques can be classifiedinto two categories i.e. linear filtering and non-linear filtering.In this paper a study is presented on some of the filtering techniqueswhich are based on linear and nonlinear approaches. These techniquesincludes different adaptive filtering based on algorithm like LMS,NLMS and RLS etc., Kalman filter, ARMA and NARMA time series applicationfor filtering, neural networks combine with fuzzy i.e. ANFIS. Thispaper also includes the application of various features i.e. MFCC,LPC, PLP and gamma for filtering and recognition

Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments

Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to traditional unsupervised approaches and with sufficient training, can alleviate the shortcomings of the unsupervised methods in various real-life acoustic environments. In this light, we review recently developed, representative deep learning approaches for tackling non-stationary additive and convolutional degradation of speech with the aim of providing guidelines for those involved in the development of environmentally robust speech recognition systems. We separately discuss single- and multi-channel techniques developed for the front-end and back-end of speech recognition systems, as well as joint front-end and back-end training frameworks

Aerial Vehicle Tracking by Adaptive Fusion of Hyperspectral Likelihood Maps

Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online manner for hyperspectral channels ranging from visible to infrared wavelengths. The key idea is that, our adaptive fusion method can combine likelihood maps from multiple bands of hyperspectral imagery into one single more distinctive representation increasing the margin between mean value of foreground and background pixels in the fused map. Experimental results show that the HLT not only outperforms all established fusion methods but is on par with the current state-of-the-art hyperspectral target tracking frameworks.Comment: Accepted at the International Conference on Computer Vision and Pattern Recognition Workshops, 201