Abstract
Retracted paper: In this paper, the basic principles of HMM, HMM studied three major issues need to be addressed as well as overflow problems in the practical application of how to solve the HMM. Because artificial neural network (ANN) with anti-noise, adaptive, learning ability, recognition speed, etc., taking into account the characteristics of the common features of speech recognition and pattern recognition and artificial neural networks have, this article will get a mixed combination of HMM in ANN model, using ANN to make up for some deficiencies of HMM. Experiments show that the hybrid model recognition rate than the HMM model increased by 4%, but the algorithm still has many defects to be resolved.
Highlights
T communications core technology "multi-user detection," has made great achievements
Using a forward algorithm [1] to calculate the probability that a particular HMM state sequence
The most likely to find a hidden state sequence based on the sequence of observable state
Summary
HMM is applied to speech recognition, text recognition computer and mobile. HMM in bioinformatics, fault diagnosis, face and other areas have begun to be applied
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
