scholarly journals On a Vector towards a Novel Hearing Aid Feature: What Can We Learn from Modern Family, Voice Classification and Deep Learning Algorithms

2021 ◽  
Vol 11 (12) ◽  
pp. 5659
Author(s):  
William Hodgetts ◽  
Qi Song ◽  
Xinyue Xiang ◽  
Jacqueline Cummine
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Hearing Aid ◽  
Machine Learning Techniques ◽  
Large Field ◽  
Basic Model ◽  
Learning Techniques ◽  
Modern Family ◽  
Popular Television ◽  
Voice Classification

(1) Background: The application of machine learning techniques in the speech recognition literature has become a large field of study. Here, we aim to (1) expand the available evidence for the use of machine learning techniques for voice classification and (2) discuss the implications of such approaches towards the development of novel hearing aid features (i.e., voice familiarity detection). To do this, we built and tested a Convolutional Neural Network (CNN) Model for the identification and classification of a series of voices, namely the 10 cast members of the popular television show “Modern Family”. (2) Methods: Representative voice samples were selected from Season 1 of Modern Family (N = 300; 30 samples for each of the classes of the classification in this model, namely Phil, Claire, Hailey, Alex, Luke, Gloria, Jay, Manny, Mitch, Cameron). The audio samples were then cleaned and normalized. Feature extraction was then implemented and used as the input to train a basic CNN model and an advanced CNN model. (3) Results: Accuracy of voice classification for the basic model was 89%. Accuracy of the voice classification for the advanced model was 99%.; (4) Conclusions: Greater familiarity with a voice is known to be beneficial for speech recognition. If a hearing aid can eventually be programmed to recognize voices that are familiar or not, perhaps it can also apply familiar voice features to improve hearing performance. Here we discuss how such machine learning, when applied to voice recognition, is a potential technological solution in the coming years.

scholarly journals Machine Learning Techniques for Speech Recognition using the Magnitude

2020 ◽  
Vol 7 (1) ◽  
pp. 33-40
Author(s):  
C. Gopala Krishnan ◽  
Y. Harold Robinson ◽  
Naveen Chilamkurti
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals Machine Learning Techniques for Speech Recognition using the Magnititudes

2020 ◽  
Vol 8 (6) ◽  
pp. 1667-1671
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Data Processing ◽  
Supervised Learning ◽  
Current Trend ◽  
Large Datasets ◽  
Point Of View ◽  
Machine Learning Techniques ◽  
Supervised And Unsupervised Learning ◽  
Learning Techniques

Speech is the most proficient method of correspondence between people groups. Discourse acknowledgment is an interdisciplinary subfield of computational phonetics that creates approaches and advances that empowers the acknowledgment and interpretation of communicated in language into content by PCs. It is otherwise called programmed discourse acknowledgment (ASR), PC discourse acknowledgment or discourse to content (STT). It consolidates information and research in the etymology, software engineering, and electrical building fields. This, being the best methodology of correspondence, could likewise be a helpful interface to speak with machines. Machine learning consists of supervised and unsupervised learning among which supervised learning is used for the speech recognition objectives. Supervised learning is that the data processing task of inferring a perform from labeled coaching information. Speech recognition is the current trend that has gained focus over the decades. Most automation technologies use speech and speech recognition for various perspectives. This paper offers a diagram of major innovative point of view and valuation for the fundamental advancement of speech recognitionand offers review method created in each phase of discourse acknowledgment utilizing supervised learning. The project will use ANN to recognize speeches using magnitudes with large datasets.

Abductive Machine Learning for Modeling and Predicting the Educational Score in School Health Surveys

1996 ◽  
Vol 35 (03) ◽  
pp. 265-271 ◽  
Author(s):  
A. M. Mangoud ◽  
R. E. Abdel-Aal
Keyword(s):  
Machine Learning ◽  
School Health ◽  
Fold Increase ◽  
Machine Learning Techniques ◽  
Primary School Children ◽  
Basic Model ◽  
Analytical Functions ◽  
Learning Techniques ◽  
Predicting Outcome ◽  
Network Approaches

Abstract:The use of modern abductive machine learning techniques is described for modeling and predicting outcome parameters in terms of input parameters in medical survey data. The AIM® (Abductory Induction Mechanism) abductive network machine-learning tool is used to model the educational score in a health survey of 2,720 Albanian primary school children. Data included the child’s age, gender, vision, nourishment, parasite infection, family size, parents’ education, and educational score. Models synthesized by training on just 100 cases predict the educational score output for the remaining 2,620 cases with 100% accuracy. Simple models represented as analytical functions highlight global relationships and trends in the survey population. Models generated are quite robust, with no change in the basic model structure for a 10-fold increase in the size of the training set. Compared to other statistical and neural network approaches, AIM provides faster and highly automated model synthesis, requiring little or no user intervention.

scholarly journals Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2326
Author(s):  
Ayesha Pervaiz ◽  
Fawad Hussain ◽  
Huma Israr ◽  
Muhammad Ali Tahir ◽  
Fawad Riasat Raja ◽  
...  
Keyword(s):  
Machine Learning ◽  
Speech Recognition ◽  
Speech Processing ◽  
Recognition System ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Noise Robustness ◽  
Human Computer Interfaces ◽  
Learning Techniques ◽  
Speech Corpora

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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