The acoustic diversity in the phoneme inventories of the world’s languages

A comparative analysis of multi-language speech samples is conducted using acoustic characteristics of phoneme realisations in spoken languages. Different approaches to investigation of phonemic diversity in the context of language evolution are compared and discussed. We introduced our approach (materials and methods) and presented preliminary results of research. We built an online database dedicated to voice acquisition and a storage of good quality speech samples collected across the globe. Software designed for automatic extraction and analysis of phonemes was developed and adapted for languages classification. Research involves both experimental and theoretical works that aim at gaining knowledge about phonetic diversity of languages across the world. Additionally, the expected results may be applied to verify the hypothesis of modern languages expansion from Africa, brought to attention by Atkinson

Some Commonly Used Speech Feature Extraction Algorithms

Speech is a complex naturally acquired human motor ability. It is characterized in adults with the production of about 14 different sounds per second via the harmonized actions of roughly 100 muscles. Speaker recognition is the capability of a software or hardware to receive speech signal, identify the speaker present in the speech signal and recognize the speaker afterwards. Feature extraction is accomplished by changing the speech waveform to a form of parametric representation at a relatively minimized data rate for subsequent processing and analysis. Therefore, acceptable classification is derived from excellent and quality features. Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT) and Perceptual Linear Prediction (PLP) are the speech feature extraction techniques that were discussed in these chapter. These methods have been tested in a wide variety of applications, giving them high level of reliability and acceptability. Researchers have made several modifications to the above discussed techniques to make them less susceptible to noise, more robust and consume less time. In conclusion, none of the methods is superior to the other, the area of application would determine which method to select