Speaker identification using vowels features through a combined method of formants, wavelets, and neural network classifiers

- This paper proposes a new method for speaker feature extraction based on Formants, Wavelet Entropy and Neural Networks denoted as FWENN.
- In the first stage, five formants and seven Shannon entropy wavelet packets are extracted from the speakers' signals as the speaker feature vector.
- In the second stage, these 12 feature extraction coefficients are used as inputs to feed-forward neural networks.
- In contrast to conventional speaker identification methods that extract features from sentences (or words), the proposed method extracts the features from vowels.
- Advantages of using vowels include the ability to identify speakers when only partially-recorded words are available. This may be useful for deaf-mute persons.