This paper is to compare two most common features representing a speech word for speech recognition on the basis of accuracy, computation time, complexity and cost. The two features to represent a speech word are the linear predict coding cepstra (LPCC) and the Mel-frequency cepstrum coefficient (MFCC). The MFCC was shown to be more accurate than the LPCC in speech recognition using the dynamic...

The aim of this work is to develop methods that enable acoustic speech features to be predicted from mel-frequency cepstral coefficient (MFCC) vectors as may be encountered in distributed speech recognition architectures. The work begins with a detailed analysis of the multiple correlation between acoustic speech features and MFCC vectors. This confirms the existence of correlation, which is fo...

In most speaker recognition systems speech utterances are not constrained in content or language. In a text-dependent speaker recognition system lexical content of speech and language are known in advance. The goal of this paper is to show that this information can be used by a segmental features (SF) approach to improve a standard Gaussian mixture model with MFCC features (GMM-MFCC). Speech fe...

The task of native language (L1) identification from nonnative language (L2) can be thought of as the task of identifying the common traits that each group of L1 speakers maintains while speaking L2 irrespective of the dialect or region. Under the assumption that speakers are L1 proficient, non-native cues in terms of segmental and prosodic aspects are investigated in our work. In this paper, w...

We investigate the uses and limitations of MFCC analysis for feature extraction from music files in the domain of genre recognition. Intra-genre and Inter-genre classification is explored. We implement a method of genre classification based on MFCC extraction, K-means clustering, and KNN analysis. We demonstrate the efficacy of our method through testing, yielding a 99% accuracy rate.

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...