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Mel-frequency cepstral coefficient analysis in speech recognition
2006 International Conference on Computing & Informatics, 2006Speech recognition is a major topic in speech signal processing. Speech recognition is considered as one of the most popular and reliable biometric technologies used in automatic personal identification systems. Speech recognition systems are used for variety of applications such as multimedia browsing tool, access centre, security and finance.
Chin Kim On +3 more
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Encrypted Domain Mel-Frequency Cepstral Coefficient and Fragile Audio Watermarking
2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE), 2018Audio has become increasingly important in modern social communication and mobile Internet, e.g., the employment of voice messaging in mobile social Apps. In the scenario of cloud computing, we need to consider the privacy protection of the audio content and the integrity of the audio simultaneously.
Jian Chen +5 more
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Identification of satellite images based on mel frequency cepstral coefficients
2009 International Conference on Computer Engineering & Systems, 2009MFCC technique is an efficient technique which can be used for speech signals' classification as MFCC can be applied for 1-D signals. This paper suggests a new application for MFCC technique as it can be used for classification of satellite images, which are 2-D objects.
T. M. Talal, Ayman El-Sayed
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2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT), 2016
Speech processing is emerged as one of the important application area of digital signal processing. Power Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) are mainly used in feature extraction of speech signals.
null Bharathi +2 more
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Speech processing is emerged as one of the important application area of digital signal processing. Power Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficient (MFCC) are mainly used in feature extraction of speech signals.
null Bharathi +2 more
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Mel-Frequency Cepstral and Linear Predictive Coefficients
2018Mel-frequency cepstral coefficients (MFCCs) and linear predictive coefficients (LPCs) are features used to describe sound according to time, frequency, and amplitude. These techniques, which are mainly used in speech analysis, are reviewed step by step for a good understanding and practice.
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Mel-frequency cepstral coefficients as features for automatic speaker recognition
2015 23rd Telecommunications Forum Telfor (TELFOR), 2015Automatic speaker recognizer can be based on the use of mel-frequency cepstral coefficients as speaker features. Mel-frequency cepstral coefficients depend on energy inside considered auditory critical bands. These auditory critical bands model masking phenomena.
Ivan D. Jokic +3 more
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Mel Frequency Cepstral Coefficients Based Similar Albanian Phonemes Recognition
2016In Albanian language there are several phonemes that are similar in pronunciation like /q/ - /c/, /rr/ - /r/, /th/ - /dh/ and /gj/ - /xh/. These phonemes are difficult to distinguish by human ear even for native speaking Albanians from different regions.
Bertan Karahoda +2 more
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Interpreting mel frequency cepstral coefficients for frication on the Turkish rhotic
The Journal of the Acoustical Society of AmericaCanonically, the “r” in Turkish is produced as an alveolar tap; however, word-finally, this tap has been described as devoiced and fricated. Previous work found that mel frequency cepstral coefficients (MFCCs) show spectral differences between canonical and fricated taps in Turkish.
Janalyn A. Miklas, Matthew C. Kelley
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Multiple time resolutions for derivatives of Mel-frequency cepstral coefficients
IEEE Workshop on Automatic Speech Recognition and Understanding, 2001. ASRU '01., 2005Most speech recognition systems are based on Mel-frequency cepstral coefficients and their first- and second-order derivatives. The derivatives are normally approximated by fitting a linear regression line to a fixed-length segment of consecutive frames. The time resolution and smoothness of the estimated derivative depends on the length of the segment.
G. Stemmer +3 more
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Journal of Intelligent & Fuzzy Systems
This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Zhang, Jiarui, Ling, Bingo Wing-Kuen
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This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
Zhang, Jiarui, Ling, Bingo Wing-Kuen
openaire +2 more sources