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HMM-based phoneme speech recognition system for the control and command of industrial robots

Naik Adwait

Technical Transactions

2021

Vol. 118, iss. 1

art. no. e2021002

n recent years, the integration of human-robot interaction with speech recognition has gained a lot of pace in the manufacturing industries. Conventional methods to control the robots include semi-autonomous, fully-autonomous, and wired methods. Operating through a teaching pendant or a joystick is easy to implement but is not effective when the robot is deployed to perform complex repetitive tasks. Speech and touch are natural ways of communicating for humans and speech recognition, being the best option, is a heavily researched technology. In this study, we aim at developing a stable and robust speech recognition system to allow humans to communicate with machines (robotic-arm) in a seamless manner. This paper investigates the potential of the linear predictive coding technique to develop a stable and robust HMM-based phoneme speech recognition system for applications in robotics. Our system is divided into three segments: a microphone array, a voice module, and a robotic arm with three degrees of freedom (DOF). To validate our approach, we performed experiments with simple and complex sentences for various robotic activities such as manipulating a cube and pick and place tasks. Moreover, we also analyzed the test results to rectify problems including accuracy and recognition score.

Speech nonfluency detection and classification based on linear prediction coefficients and neural networks

Kobus A., Kuniszyk-Jóźkowiak W., Smołka E., Codello I.

Journal of Medical Informatics & Technologies

2010

Vol. 15

135--143

The goal of the paper is to present a speech nonfluency detection method based on linear prediction coefficients obtained by using the covariance method. The application “Dabar” was created for research. It implements three different methods of LP with the ability to send coefficients computed by them into the input of Kohonen networks. Neural networks were used to classify utterances in categories of fluent and nonfluent. The first one was Kohonen network (SOM), used to reduce LP coefficients representation of each window, which were used as input data to SOM input layer, to a vector of winning neurons of SOM output layer. Radial Basis Function (RBF) networks, linear networks and Multi-Layer Perceptrons were used as classifiers. The research was based on 55 fluent samples and 54 samples with blockades on plosives (p, b, d, t, k, g). The examination was finished with the outcome of 76% classifying.

Rozpoznawanie komend głosowych za pomocą sieci neuronowych

Duda J.

Śląskie Wiadomości Elektryczne

2004

Nr 2 (53)

21--24

W artykule zostały przedstawione informacje na temat budowy systemu rozpoznawania komend głosowych. Dokładniej omówiono: przygotowanie wzorców głosowych za pomocą Liniowego Kodowania Predykcyjnego (LPC) oraz metodę ich klasyfikacji za pomocą sieci neuronowych. Przedstawiono też program, który został napisany w celu sprawdzenia możliwości zastosowana sieci neuronowych do rozpoznawania komend głosowych.