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Using Mel-Frequency Cepstral Coefficients in Missing Data Technique
2004
EURASIP Journal on Advances in Signal Processing
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In this paper, we propose a hybrid approach based on the marginalisation and the soft decision techniques that make use of the Mel-frequency cepstral coefficients (MFCCs) instead of filter bank coefficients ...
Filter bank is the most common feature being employed in the research of the marginalisation approaches for robust speech recognition due to its simplicity in detecting the unreliable data in the frequency ...
Applying missing data techniques to cepstral features is obviously attractive and natural. ...
doi:10.1155/s1110865704309030
fatcat:leuy5kdswjapjaqjrc7tbg5mne
Nondestructive Determination of Maturity of the Monthong Durian by Mel-Frequency Cepstral Coefficients (MFCCs) and Neural Network
2016
Applied Mechanics and Materials
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After that the knocked-sound was analyzed and generated to Mel-frequency cepstral coefficients (MFCCs) that is used to train data for the classifier. ...
The ability to select only good quality Durian without cutting or cleaving is useful because buyers will not waste money ordering undesirable Durian.This paper proposes a nondestructive technique to determine ...
After that the knocked-sound was analyzed and generated to Mel-frequency cepstral coefficients (MFCCs) that is used to train data for the classifier. ...
doi:10.4028/www.scientific.net/amm.855.75
fatcat:jlfsjpbuobhrlim4lnkov2nxri
Wavelet Packet based Mel Frequency Cepstral Features for Text Independent Speaker Identification
2017
Figshare
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The present research proposes a paradigm which combines the Wavelet Packet Transform (WPT) with the distinguished Mel Frequency Cepstral Coefficients (MFCC) for extraction of speech feature vectors in ...
The identification results of the MFCC fea- tures and the Wavelet Packet based Mel Frequency Cepstral (WP-MFC) Features are compared to validate the efficiency of the proposed paradigm. ...
Mel Frequency Cepstral Coefficients: The advent of Mel Frequency Cepstral Coefficient (MFCC) technique for the task of feature extraction has over shadowed the existence of majority of its predecessor ...
doi:10.6084/m9.figshare.5327575.v1
fatcat:ifs575n4anhufhu3p26yusmxuq
Detection of AI-Synthesized Speech Using Cepstral Bispectral Statistics
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2009.01934v1 fatcat:u4tplcww2bapjlac4dhowq2fyy
Also, Cepstral analysis revealed a durable power component in human speech that is missing for a synthesized speech. ...
Higher-order statistics have less correlation for human speech in comparison to a synthesized speech. ...
Analysis of Mel Frequency Cepstral Coefficient (MFCC) and visualization using the Mel spectrogram is described. Delta and Delta Square related to Mel Cepstrum is briefed.
A. ...
arXiv:2009.01934v2
fatcat:ylp4wlyddng3xiefuwibzom3k4
Speech Enhancement Using Pitch Detection Approach For Noisy Environment
[article]
2013
arXiv
pre-print
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Therefore, the proposed system can be used in an application designed for mathematical symbol recognition (especially symbols not available on the keyboard) in schools. ...
As in some cases speaking may be more convenient than typing, even for rapid typists: many mathematical symbols are missing from the keyboard but can be easily spoken and recognized. ...
Mel-Frequency Cepstral Coefficients (MFCC) Mel-frequency Cepstral coefficient is one of the most prevalent and popular method used in the field of voice feature extraction. ...
arXiv:1305.2352v1
fatcat:6k57i7f43fbylejiq72h2s2nx4
GMM based Language Identification using MFCC and SDC Features
2014
International Journal of Computer Applications
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In this paper, a baseline system for the LID system in multilingual environments has been developed using GMM as a classifier and MFCC combined with Shifted-Delta-Cepstral (SDC) as front end processing ...
the combining features of MFCC with SDC over the baseline systems that using MFCC and SDC features in individual respectively. ...
Mel-Frequency Cepstral Coefficients (MFCC) Feature Mel-Frequency Cepstral Coefficients (MFCC) is one of the most popular approaches of feature extraction technique in both speaker recognition and LID system ...
doi:10.5120/14840-3103
fatcat:pffq2mfqhrduzj2hfsuld3nlvy
Training Wideband Acoustic Models Using Mixed-Bandwidth Training Data for Speech Recognition
2007
IEEE Transactions on Audio, Speech, and Language Processing
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One serious difficulty in the deployment of wideband speech recognition systems for new tasks is the expense in both time and cost of obtaining sufficient training data. ...
In this paper, we propose a novel expectation-maximization (EM) algorithm in which wideband acoustic models are trained using a small amount of wideband speech and a larger amount of narrowband speech. ...
FEATURE EXTRACTION FOR ASR In this paper, we assume that mel-frequency cepstral coefficients (MFCCs) are the features used for recognition. ...
doi:10.1109/tasl.2006.876774
fatcat:qjzkvcdc7bayxgzbpkkzytysqu
Throat to Acoustic Speech Mapping for Spectral Parameter Correction using Artificial Neural Network Approach
2020
Proceedings of International Exchange and Innovation Conference on Engineering & Sciences (IEICES)
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The proposed algorithm is tested using ATR503 Dataset. The simulation results show a noticeable performance in the field of speech communication in adverse environments. ...
This paper exploits the nonlinear mapping property of Multi-Layered Feed Forward Neural Network (MLFFNN) for estimation of high-frequency components (4-8kHz) from the low-frequency band (0-4kHz) of TM ...
The Mel Frequency
Cepstral Coefficients (MFCCs) feature extraction
method is performed on the speech signals to extract the
cepstral coefficients are derived [6]. ...
doi:10.5109/4102497
fatcat:gooawqhoeje7hhgyjf4c7zbg3e
Selective Gammatone Envelope Feature for Robust Sound Event Recognition
2012
IEICE transactions on information and systems
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Conventional features for Automatic Speech Recognition and Sound Event Recognition such as Mel-Frequency Cepstral Coefficients (MFCCs) have been shown to perform poorly in noisy conditions. ...
In the experiments with Hidden Markov Model (HMM) recognizers, we shall show that our feature outperforms MFCCs significantly in four different noisy environments at various signal-to-noise ratios. ...
Gammatone Cepstral Coefficients The gammatone filterbank output replaces the Mel filterbank output used in MFCCs. ...
doi:10.1587/transinf.e95.d.1229
fatcat:z5rexvl4djgt5ny2a3qrc4nrqq
合成單元與問題集之定義於隱藏式馬可夫模型中文歌聲合成系統之建立 (Synthesis Unit and Question Set Definition for Mandarin HMM-based Singing Voice Synthesis)
2013
Taiwan Conference on Computational Linguistics and Speech Processing
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The spectrum parameter vectors consist of 49th-order STRAIGHT mel-cepstral coefficients including the zero-th coefficient, their delta, and delta-delta coefficients. ...
In the baseline system, the most frequently used questions of F0 and mel-cepstral clustering trees are sub-syllables types, position of note and phrase level. ...
Furthermore, we used three methods to refine our system, i.e. question set definition, pitch-shift pseudo data extension and vibrato post-processing. ...
dblp:conf/rocling/ChengHW13
fatcat:ggkusq4fbfhg5d7ly5a54zjilu
Optimization of Telephone Handset Distortion in Speaker Recognition by Discriminative Feature Design
2014
IOSR Journal of Electronics and Communication Engineering
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This technique used mel-cepstral features, log spectrum and prosody based features with a nonlinear artificial neural network in designing speaker recognition features that minimize telephone handset distortion ...
Effect of handset distortion was done to maximize speaker recognition performance specifically in the setting of telephone handset mismatch between training and testing as results on the 1998 NIST Speaker ...
The mel-cepstral coefficients were computed by applying a sliding 25 ms window to the speech, resulting in a frame of speech every 10 ms. ...
doi:10.9790/2834-09523136
fatcat:4xymka7fzfhjhb5xg7vqz4fv5i
Distinctive features for normal and crackles respiratory sounds using cepstral coefficients
2019
Bulletin of Electrical Engineering and Informatics
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In this paper, Linear Predictive Cepstral Coefficient (LPCC) and Mel-frequency Cepstral Coefficient (MFCC) are used to extract features from normal and crackles respiratory sounds. ...
This is because in MFCC analysis, mel scale is used to wrap the frequency and it is approximately close to the human auditory perception [7] . ...
The formula to convert the signal from frequency to Mel-Scale frequency is shown in (3) : ( ) ( ) (3) The formula to convert Mel-Scale frequency to frequency in Hz is shown in (4): ( ) ( ) (4) Discrete ...
doi:10.11591/eei.v8i3.1517
fatcat:h6ooads6sfcdfpvrraeic7dm7i
Advances in Missing Feature Techniques for Robust Large-Vocabulary Continuous Speech Recognition
2011
IEEE Transactions on Audio, Speech, and Language Processing
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The proposed system operates on either binary masks where hard decisions are made about the reliability of the data or on fuzzy masks which use a soft decision criterion. ...
Channel compensation in speech recognition typically involves estimating an additive shift in the log-spectral or cepstral domain. ...
, spectral peaks [8] and Mel-frequency cepstral coefficients (MFCCs) [9] , which later became the standard feature set in ASR. ...
doi:10.1109/tasl.2010.2045235
fatcat:yrhim5bjxneajablpdoy4h3bza
High Security and Capacity of Image Steganography for Hiding Human Speech Based on Spatial and Cepstral Domains
2020
ARO. The Scientific Journal of Koya University
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The suggested technique of image steganography is achieved using both spatial and cepstral domains, where the Mel-frequency cepstral coefficients (MFCCs) are adopted, as very efficient features of the ...
Second is to improve the data security by hiding the secret data (MFCCs features) anywhere in the host image rather than directly using the least significant bits substitution of the cover image. ...
cepstral coefficients
TABLE II Nbits II Used in Both Direct and MFCCs Techniques MFCCs: Mel-frequency cepstral coefficients, Nbits: Number of bits Fig. 13. ...
doi:10.14500/aro.10670
fatcat:yin2i5wiqbax5a6ajsnwq6b6fi
Low-variance Multitaper Mel-frequency Cepstral Coefficient Features for Speech and Speaker Recognition Systems
2012
Cognitive Computation
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In this paper we investigate low-variance multi-taper spectrum estimation methods to compute the mel-frequency cepstral coefficient (MFCC) features for robust speech and speaker recognition systems. ...
In a speaker verification task, compared to the Hamming window technique, the sinusoidal weighted cepstrum estimator (SWCE), Multi-peak, and Thomson multi-taper techniques provide a relative improvement ...
Currently, the most widely used speech features both in speaker and speech recognition systems are the Mel-frequency cepstral coefficient (MFCC) [1] and perceptual linear predictive (PLP) features [ ...
doi:10.1007/s12559-012-9197-5
fatcat:6fg6w4dmbbhj7dnk6l2kect5ny
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