Text Independent Amharic Language Speaker Identification In Noisy Environments using speech Processing Techniques
- Abrham Debasu Mengistu, Dagnachew Melesew Alemayehu
This paper presents a neural network based text-dependent speaker identification system for Thai language. Linear Prediction Coefficients (LPC) are extracted from speech signal and formed feature vectors. These features are fed into multilayer perceptron (MLP) neural network with backpropagation learning algorithm for training and identification processes. Five Thai tone marks are considered very closely in choosing the sentences in order to achieve the best speaker identification accuracy. Five speaking texts with each Thai tone and a mixed tone text are comparatively experimented. Average identification rate on 9 speakers achieves above 95% when using mixed tone text, and poor results occur with middle and low tone texts, which usually cause vagueness or unclear voices.