Abstract—We use speech recognition algorithms daily with our phones, computers, home assistants, and more. Each of these systems use algorithms to convert the sound waves into useful data for processing which is [then interpreted by the machine. Some of these machines use older algorithms while the newer systems use neural networks to interpret this data. These systems then produce an output generated in the form of text to be used. A large amount of training data is needed to make these algorithms and neural networks function effectively.
Index Terms—Speech recognition, neural network, Markov model, Gaussian mixture model, principal component analysis, K-means clustering, EM algorithm, maximum likelihood, MIXFIT.
The authors are with the Valdosta State University, Valdosta, GA 31698 USA (e-mail: svault@valdosta.edu, reperez@valdosta.edu, cakimble@valdosta.edu, jwang@valdosta.edu).
Cite: Shaun V. Ault, Rene J. Perez, Chloe A. Kimble, and Jin Wang, "On Speech Recognition Algorithms," International Journal of Machine Learning and Computing vol. 8, no. 6, pp. 518-523, 2018.