Over the past decades, extensive research has been carried out on various possible implementations of automatic speech recognition (ASR) systems. The most renowned algorithms in the field of ASR are the mel-frequency cepstral coefficients and the hidden Markov models. However, there are also other methods, such as wavelet-based transforms, artificial neural networks and support vector machines, which are becoming more popular. This review article presents a comparative study on different approaches that were proposed for the task of ASR, and which are widely used nowadays.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2012.0151

Comparative study of automatic speech recognition techniques

Recently great strides have been made in the field of automatic speech recognition (ASR) by using various deep learning techniques. In this study, we present a thorough comparison between cutting-edged techniques currently being used in this area, with a special focus on the various deep learning methods. This study explores different feature extraction methods, state-of-the-art classification models, and vis-a-vis their impact on an ASR. As deep learning techniques are very data-dependent different speech datasets that are available online are also discussed in detail. In the end, the various online toolkits, resources, and language models that can be helpful in the formulation of an ASR are also proffered. In this study, we captured every aspect that can impact the performance of an ASR. Hence, we speculate that this work is a good starting point for academics interested in ASR research.

Automatic speech recognition: a survey

Segmentation and classification in MRI and US fetal imaging: Recent trends and future prospects.

Automated Screening of Fetal Heart Chambers from 2-D Ultrasound Cine-Loop Sequences

This paper gives a brief survey on speech recognition and presents an overview for various techniques used at various stages of speech recognition systems. Researchers has been working in this research area for many years however accuracy for speech recognition still attention for variation of context, speaker’s variability, environment conditions .The development of speech recognition system requires certain concepts to be included-Defining different classes of speech, techniques for speech feature extraction, speech classification modeling and measuring system performance .The main aim of this paper is to discuss and compare different approaches used for feature extraction and classification stages in speech recognition system.

/pdf/speech-feature-extraction-and-classification-a-comparative-3mkf2bz0v0.pdf

Speech Feature Extraction and Classification: A Comparative Review

The paper presents a research on detection of cardiac structures in echocardiography gray images from fetal hearts. Pattern recognition and a density probability function of scales of gray is used for search cardiac structures and then compared with pattern structures previously stored. The Bhattacharyya coefficient is used in order to obtain the similarity to define the choice of the structures of interest. The method uses also texture features to isolate the region of interest inside the ultrasound image to improve the results and performance. The prototype developed to evaluate the method and some validated results are presented.

Cardiac Structure Recognition in Ultrasound Images

This paper argues that connectionist systems are a good approach to implement a speech understanding computational model. In this direction, we propose SUM, a Speech Understanding Model, which is a software architecture based on recent neurocognitive researches. The SUM's computational implementation applies wavelets transforms to speech signal processing and connectionist models to syntactic parsing and prosodic-semantic mapping. This approach enables a system of analysis by compensation, when the syntactic analysis does not offer a good reliable level, it is possible to evaluate prosodic-smeantic analysis, such as in human speech understanding.

A Connectionist Approach to Speech Understanding

Fetal echocardiography is an important tool for diagnosis. This paper presents a method to provide automatic localization of cardiac structures in fetal echocardiography images, where the early diagnostics of heart congenital diseases can improve results from medical treatment. The interest structures are the four cardiac cavities. The method use a hierarchical search based on a windowing technique with mold matching to find the cardiac cavities. This mold is calculated using a probability density function that receives as parameter the gray level of a representative image and also uses spatial features of the images. The hierarchical search is performed: (i) first, the image is covered to locate the heart; (ii) then, based on the heart position, the desired structure is found. The comparison of the mold and the candidate image is made using the Euclidean Distance. One of the main characteristics of the method is its rotation invariance.

M.L. de Siqueira

Papers

Cardiac Structure Recognition in Ultrasound Images

A Connectionist Approach to Speech Understanding

Localization of cardiac cavities using hierarchically search in 2D grayscale ultrasound images