Domestic Language Accent Detector Using MFCC and GMM
نویسندگان
چکیده
منابع مشابه
New Features for Language Identification Using Gmm
Automatic Language Identification (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the language. Most of the existing LID systems are based on MFCC feature vectors. This paper introduces the use of new feature extraction approach ...
متن کاملGMM Classifier for Identification of Neurological Disordered Voices Using MFCC Features
Automatic detection of neurological disordered subjects voice mostly relies on parameters extracted from time-domain processing. The calculation of these parameters often requires prior pitch period estimation; which in turn depends heavily on the robustness of pitch detection algorithm. In the present work cepstraldomain processing technique which does not require pitch estimation has been ado...
متن کاملLanguage and Text-Independent Speaker Identification System Using GMM
This paper motivates the use of Dynamic Mel-Frequency Cepstral Coefficient (DMFCC) feature and combination of DMFCC and MFCC features for robust language and text-independent speaker identification. MFCC feature, modeled on the human auditory system has been the widely used feature for speaker recognition because of its less vulnerability to noise perturbation and little session variability. Bu...
متن کاملSpeaker Accent Recognition by MFCC Using K- Nearest Neighbour Algorithm: A Different Approach
A K-Nearest Neighbour Algorithm involving Mel-Frequency Cepstral Coefficients (MFCCs) is provided to perform Speech signal feature extraction for the task of speaker accent recognition. Mel-Frequency Cepstral Coefficient is effectively used to perform the feature extraction of the input signal. For each input signal the mean of the MFCC matrix is used for pattern recognition .The K-nearest neig...
متن کاملSpeaker, Accent, and Language Identification Using Multilingual Phone Strings
Currently, approaches based on Gaussian Mixture Models (GMMs) [4] are the most widely and successfully used methods for speaker identification. Although GMMs have been applied successfully to close-speaking microphone scenarios under matched training and testing conditions, their performance degrades dramatically under mismatched conditions. The term “mismatched condition” describes a situation...
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: International Journal of Applied Engineering Research
سال: 2020
ISSN: 0973-4562,0973-9769
DOI: 10.37622/ijaer/15.8.2020.800-803