Abstract
Connectionist models, also known as neural networks, have been widely studied during the past few years. Applications concern various tasks in the fields of pattern recognition and signal processing, especially automatic speech recognition. This chapter presents the basic properties of these models and the different problems in the area of speech recognition to which they have been applied so far. The classical models of neural networks are also briefly recalled. We then concentrate on a particular model grounded on neuro-biological data, the cortical column. The characteristics of the model are given and we then present the architectures of two systems based on the cortical column for solving two different problems of speech recognition, i.e. acoustic-phonetic decoding and isolated word recognition.
Sign-in/Register to access full text options 
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.
