Some Commonly Used Speech Feature Extraction Algorithms

Abstract

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.

ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation

“…Mel-frequency cepstral coefficients (MFCCs) are widely used to extract features for voice-based authentication [ 13 , 14 , 15 , 16 , 17 , 18 ]. MFCCs are obtained by extracting features from the audio signal, and when used as input to the base model, they produce much better performance than when directly considering raw audio signals as input.…”

G-Cocktail: An Algorithm to Address Cocktail Party Problem of Gujarati Language using CatBoost

“…Autocorrelation coefficients are aliased in conventional linear prediction. The susceptibility of LPC estimates to quantization noise is high, so they are not well suited for generalization [19].…”

“…It can derive information from latent signals in both the time and frequency domains at the same time. Many wavelets are orthogonal, which is an outstanding feature for compact signal representation [19] [20]. The wavelet transform breaks down a signal into a set of simple functions known as wavelets.…”

“…LPC are a type of speech features that imitates the human vocal tract. It estimates the concentration and frequency of the left-over residue by approximating the formants, removing their effects from the speech signal, and evaluating the speech signal [18] [19]. Each sample of the signal is stated to be a direct incorporation of previous samples in the result.…”

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“…To extract the vocal features like human ear the algorithm should replicate the human acoustics. MFCC, LPC, Linear Prediction Cepstral Coefficients (LPCC), Linear Spectral Frequencies (LSF), Perceptual Linear prediction (PLP) imitate the human hearing and speaking tract and give relevant features [10]. MFCC filters frequencies linearly at low frequencies and logarithmically at high frequencies to preserve the phonetically vital properties of the speech signal.…”