By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.

By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.

An algorithm used to extract HMM parameters is revisited. Most parts of the extraction process are taken from implemented Hidden Markov Toolkit (HTK) program under name HInit. The algorithm itself shows a few variations compared to another domain of implementations. The HMM model is introduced briefly based on the theory of Discrete Time Markov Chain. We schematically outline the Viterbi method implemented in HTK. Iterative definition of the method which is ready to be implemented in computer programs is reviewed. We also illustrate the method calculation precisely using manual calculation and extensive graphical illustration. The distribution of observation probability used is simply independent Gaussians r.v.s. The purpose of the content is not to justify the performance or accuracy of the method applied in a specific area. This writing merely to describe how the algorithm is performed. The whole content should enlighten the audience the insight of the Viterbi Extraction method used by HTK.

As members of the deep learning R&D team at SVDS, we are interested in comparing Recurrent Neural Network (RNN) and other approaches to speech recognition. Until a few years ago, the state-of-the-art for speech recognition was a phonetic-based approach including separate components for pronunciation, acoustic, and language models. Typically, this consists of n-gram language models combined with Hidden Markov models (HMM). We wanted to start with this as a baseline model, and then explore ways to combine it with newer approaches such as Baidu's Deep Speech. While summaries exist explaining these baseline phonetic models, there do not appear to be any easily-digestible blog posts or papers that compare the tradeoffs of the different freely available tools.