Speech to text conversion for multilingual languages

摘要

The current work presents a multilingual speech-to-text conversion system. Conversion is based on information in speech signal. Speech is the natural and most important form of communication for human being. Speech-To-Text (STT) system takes a human speech utterance as an input and requires a string of words as output. The objective of this system is to extract, characterize and recognize the information about speech. The proposed system is implemented using Mel-Frequency Cepstral Coefficient (MFCC) feature extraction technique and Minimum Distance Classifier, Support Vector Machine (SVM) methods for speech classification. Speech utterances are pre-recorded and stored in a database. Database mainly divided into two parts testing and training. Samples from training database are passed through training phase and features are extracted. Combining features for each sample forms feature vector which is stored as reference. Sample to be tested from testing part is given to system and its features are extracted. Similarity between these features and reference feature vector is computed and words having maximum similarity are given as output. The system is developed in MATLAB (R2010a) environment.