A systematic approach for segmenting voiced/unvoiced signals using fuzzy-logic system and general fusion of neural network models for phonemes-based speech recognition
Affiliations: [a] Department of Mechatronics Engineering, AMA International University, Bahrain
| [b] Principal, Sri Ramakrishna Institute of Technology, India
| [c]
Centre for International Languages, Universiti Malaysia Perlis, Malaysia
| [d] Electrical, Electronics & Automation Section, UniKL Malaysian Spanish Institute (UniKL MSI), Malaysia
Correspondence:
[*]
Corresponding author. Sathees Kumar Nataraj, Department of Mechatronics Engineering, AMA International University, Bahrain. E-mail: [email protected].
Abstract: In this paper, a speech-to-text translation model has been developed for Malaysian speakers based on 41 classes of Phonemes. A simple data acquisition algorithm has been used to develop a MATLAB graphical user interface (GUI) for recording the isolated word speech signals from 35 non-native Malaysian speakers. The collected database consists of 86 words with 41 classes of phoneme based on Affricatives, Diphthongs, Fricatives, Liquid, Nasals, Semivowels and Glides, Stop and Vowels. The speech samples are preprocessed to eliminate the undesirable artifacts and the fuzzy voice classifier has been employed to classify the samples into voiced sequence and unvoiced sequence. The voiced sequences are divided into frame segments and for each frame, the Linear Predictive co-efficients features are obtained from the voiced sequence. Then the feature sets are formed by deriving the LPC features from all the extracted voiced sequences, and used for classification. The isolated words chosen based on the phonemes are associated with the extracted features to establish classification system input-output mapping. The data are then normalized and randomized to rearrange the values into definite range. The Multilayer Neural Network (MLNN) model has been developed with four combinations of input and hidden activation functions. The neural network models are trained with 60%, 70% and 80% of the total data samples. The neural network architecture was aimed at creating a robust model with 60%, 70%, and 80% of the feature set with 25 trials. The trained network model is validated by simulating the network with the remaining 40%, 30%, and 20% of the set. The reliability of trained network models were compared by measuring true-positive, false-negative, and network classification accuracy. The LPC features show better discrimination and the MLNN neural network models trained using the LPC spectral band features gives better recognition.
Keywords: Fuzzy voice classifier, Malaysian English pronunciation, linear predictive coefficients (LPCC), neural network models (MLNN).
