Designing Optimal Phoneme-Wise Fuzzy Cluster Analysis
A large number of pattern classification algorithms and methodologies have been proposed for the phoneme recognition task during the last decades. The current paper presents a prototype distance-based fuzzy classifier, optimized for the needs of phoneme recognition. This is accomplished by the specially designed objective function and a respective training strategy. Particularly, each phonemic class is represented by a number of arbitrary-shaped clusters which adaptively match the corresponding features space distribution. The formulation of the approach is capable of delivering a variety of related conclusions based on fuzzy logic arithmetic. An overview of the inference capability is presented in combination with performance results for the Greek language.
Click to purchase paper as a non-member or login as an AES member. If your company or school subscribes to the E-Library then switch to the institutional version. If you are not an AES member and would like to subscribe to the E-Library then Join the AES!
This paper costs $33 for non-members and is temporarily free for AES members.