Objectives: The main objective is to propose a multimodal biometric system by forming fusion of Face and Speech modalities using DTCWT+QFT techniques for face MFCC+RASTA Techniques recognitions. experimental results are compared with existing works analysed the performance counterparts. Methods: proposed model, make use DTCWT QFT extract features images perform both. MFCC RASTA implemented spee...

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...

Mel frequency cepstral coefficients (MFCC) are the most widely used speech features in automatic speech recognition systems, primarily because the coefficients fit well with the assumptions used in hidden Markov models and because of the superior noise robustness of MFCC over alternative feature sets such as linear prediction-based coefficients. The authors have recently introduced human factor...

Front-end or feature extractor is the first component in an automatic speaker recognition system. Feature extraction transforms the raw speech signal into a compact but effective representation that is more stable and discriminative than the original signal. Since the front-end is the first component in the chain, the quality of the later components (speaker modeling and pattern matching) is st...

We present a quantum mechanical approach to study protein-ligand binding structure with application to a Adipocyte lipid-binding protein complexed with Propanoic Acid. The present approach employs a recently develop molecular fractionation with a conjugate caps (MFCC) method to compute protein-ligand interaction energy and performs energy optimization using the quasi-Newton method. The MFCC met...

Recently, neural network technology has shown remarkable progress in speech recognition, including word classification, emotion and identity recognition. This paper introduces three novel speaker recognition methods to improve accuracy. The first method, called long short-term memory with mel-frequency cepstral coefficients for triplet loss (LSTM-MFCC-TL), utilizes MFCC as input features the LS...

Fitur Mel-frequency cepstral coefficients (MFCC) dan teknik pengelasan berdasarkan pembelajaran mesin sering digunakan dalam mengelaskan sebutan huruf-huruf hijaiyah. Walaupun begitu, kajian-kajian lepas, prestasi ketepatan huruf hijaiyah masih lagi rendah walaupun dengan penggunaan algoritma fitur MFCC. Oleh itu, kajian khas untuk menganalisis yang sesuai akan dibincangkan kertas ini. Selain b...

This paper describes a hybrid technique for speaker recognition. Speaker recognition is that the method of identifying the person based on characteristics like pitch, tone, Cepstral coefficients in the speech wave. Here DWT and MFCC technique is employed for feature extraction. A mix of two or lot of techniques is named hybrid technique. DWT means divide the speech signal completely into differ...

In this paper, the feasibility of a system developed for the remote diagnosis of voice pathologies is analysed. More specifically, the performance of MFCC-based pathology detectors over speech transmitted through an analogue telephone channel is studied. Results indicate that MFCC are voice features fairly robust to amplitude distortion and almost insensitive to phase distortion, but the effici...

In this paper, a drive-by damage detection methodology for high-speed railway (HSR) bridges is addressed, to appraise the application of Mel-frequency cepstral coefficients (MFCC) extract Damage Index (DI). A finite element (FEM) 2D VTBI model that incorporates train, ballasted track and bridge behavior presented. The formulation includes irregularities damaged condition induced in specified st...