منابع مشابه
Feature Extraction Using Mfcc
Mel Frequency Ceptral Coefficient is a very common and efficient technique for signal processing. This paper presents a new purpose of working with MFCC by using it for Hand gesture recognition. The objective of using MFCC for hand gesture recognition is to explore the utility of the MFCC for image processing. Till now it has been used in speech recognition, for speaker identification. The pres...
متن کاملIntoxicated Speech Detection using MFCC Feature Extraction and Vector Quantization
This study has been done on a technique which is suitable for tapping the telephonic conversation from a remote location to identify intoxication and consequent impaired brain activity that may cause criminal events e.g. DUI (driving under influence). This technique is time efficient, easy to use, non–invasive for the peoples and affordable for law enforcement personnel, bartenders/servers, cou...
متن کاملMFCC and Prosodic Feature Extraction Techniques:
In this paper our main aim to provide the difference between cepstral and non-cepstral feature extraction techniques. Here we try to cover-up most of the comparative features of Mel Frequency Cepstral Coefficient and prosodic features. In speaker recognition, there are two type of techniques are available for feature extraction: Short-term features i.e. Mel Frequency Cepstral Coefficient (MFCC)...
متن کاملDetermination Of Disfluencies Associated In Stuttered Speech Using MFCC Feature Extraction
Stuttering also known as stammering is fluency disorder in which it affects the flow of speech, an involuntary repetitions, prolongation of sounds, syllables, phrase or words, and involuntary silent pause or blocks in communication. This involuntary speech disorder involves frequent and significant problems with the normal fluency and flow of speech. The number of disfluencies present in a spee...
متن کاملSVM Scheme for Speech Emotion Recognition using MFCC Feature
Emotion recognition from speech has developed as a recent research area in Human–Computer Interaction. The objective of this paper is to use a 3-stage Support Vector Machine classifier to classify seven different emotions present in the Berlin Emotional Database. For the purpose of classification, MFCC features from all the 535 files present in the database are extracted. Nine statistical measu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal & Image Processing : An International Journal
سال: 2013
ISSN: 2229-3922,0976-710X
DOI: 10.5121/sipij.2013.4408