Mel-frequency cepstral coefficients are widely used as the feature for speech recognition. In MFCC extraction process, the spectrum, obtained by Fourier transform of input speech signal is divided by mel-frequency bands, and each ban energy is extracted for the each frequency band. The coefficients are extracted by the discrete cosine transform of the obtained band energy. In this paper, we cal...

pyAudioProcessing is a Python based library for processing audio data, constructing and extracting numerical features from audio, building testing machine learning models, classifying data with existing pre-trained classification models or custom user-built models. MATLAB popular language of choice vast amount research in the speech domain. On contrary, remains majority functionality. This cont...

Speaker’s audio is one of the unique identities speaker. Nowadays not only humans but machines can also identify by their audio. Machines different properties human voice and classify speaker from speaker’s Speaker recognition still challenging with degraded limited dataset. be identified effectively when feature extraction more accurate. Mel-Frequency Cepstral Coefficient (MFCC) mostly used me...

Ameliorating the performances of speech recognition system is a challenging problem interesting recent researchers. In this paper, we compare two extraction methods of Mel Frequency Cepstral Coefficients used to represent stressed speech utterances in order to obtain best performances. The first method known as traditional is based on single window (taper) generally the Hamming window and the s...

Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, detection useful in order to attenuate mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coef...

Mel Frequency Cepstral Coefficient (MFCC) features are widely used as acoustic features for speech recognition as well as speaker recognition. In MFCC feature representation, the Mel frequency scale is used to get a high resolution in low frequency region, and a low resolution in high frequency region. This kind of processing is good for obtaining stable phonetic information, but not suitable f...

This paper deals with LP based Mel-Generalized cepstrum which has been used as front-end for Hidden Markov Model (HMM) based speech recognition and it incorporates equal-loudness power law as well as auditory-like frequency resolution. To utilize the generalized cepstral representation, the model spectrum can be varied continuously from the all-pole spectrum to that represented by the cepstrum ...

Cepstrum coefficients are widely used as features for both speech and music. In this paper, the use of discrete cepstrum coefficients is considered, which are computed from sinusoidal peaks in the short time spectrum. These coefficients are very interesting as features for pattern recognition applications since they allow to represent spectra by points in a multidimensional vector space. A new ...

This paper shows a comparison between the macro and micro mechanical model, proposed by Neely and Kim, and extended by Elliot and Ku vs. mechanical fluid model proposed by Lesser and Berkeley both used in ASR tasks. These models are used to set the central frequencies of a bank filter to obtain parameters from the speech in a similar form as MFCC (Mel Frequency Cepstrum Coefficients) has been c...

The study performs feature extraction for isolated word recognition using Mel-Frequency Cepstral Coefficient (MFCC) for Gujarati language. It explains feature extraction methods MFCC and Linear Predictive Coding (LPC) in brief. The paper compares the performances of MFCC and LPC features under Vector Quantization (VQ) method. The dataset comprising of males and females voices were trained and t...