scholarly journals AISpeech-SJTU Accent Identification System for the Accented English Speech Recognition Challenge

2021 ◽  
Author(s):  
Houjun Huang ◽  
Xu Xiang ◽  
Yexin Yang ◽  
Rao Ma ◽  
Yanmin Qian
Keyword(s):  
Speech Recognition ◽  
Identification System ◽  
Accented English ◽  
Accent Identification

scholarly journals Speech Recognition of Moroccan Dialect Using Hidden Markov Models

2019 ◽  
Vol 8 (1) ◽  
pp. 7 ◽  
Author(s):  
Mouaz Bezoui
Keyword(s):  
Speech Recognition ◽  
Euclidean Distance ◽  
Speaker Identification ◽  
Arab World ◽  
Training Session ◽  
Arab Countries ◽  
Identification System ◽  
Training Phase ◽  
Standard Arabic ◽  
Testing Phase

<p>This paper addresses the development of an Automatic Speech Recognition (ASR) system for the Moroccan Dialect. Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. In fact, Moroccan Dialect is very different from the Modern Standard Arabic (MSA) because it is highly influenced by the French Language. It is observed throughout all Arab countries that standard Arabic widely written and used for official speech, news papers, public administration and school but not used in everyday conversation and dialect is widely spoken in everyday life but almost never written. we propose to use the Mel Frequency Cepstral Coefficient (MFCC) features to specify the best speaker identification system. The extracted speech features are quantized to a number of centroids using vector quantization algorithm. These centroids constitute the codebook of that speaker. MFCC’s are calculated in training phase and again in testing phase. Speakers uttered same words once in a training session and once in a testing session later. The Euclidean distance between the MFCC’s of each speaker in training phase to the centroids of individual speaker in testing phase is measured and the speaker is identified according to the minimum Euclidean distance. The code is developed in the MATLAB environment and performs the identification satisfactorily.</p>

Automatic speech recognition of multiple accented English data

2010 ◽  
Author(s):  
Dimitra Vergyri ◽  
Lori Lamel ◽  
Jean-Luc Gauvain
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Accented English

scholarly journals INDIVIDUAL IDENTIFICATION SYSTEM DESIGN THROUGH VOICE USING LINEAR PREDICTIVE CODING METHOD AND K-NEAREST NEIGHBOR

2021 ◽  
Vol 2 (2) ◽  
pp. 95-100
Author(s):  
Davita Nadia Fadhilah ◽  
Rita Magdalena ◽  
Sofia Sa’idah
Keyword(s):  
Speech Recognition ◽  
Nearest Neighbor ◽  
Predictive Coding ◽  
Voice Recognition ◽  
Individual Identification ◽  
Identification System ◽  
K Nearest Neighbor ◽  
Linear Predictive Coding ◽  
Distance Method ◽  
K Value

Humans have a variety of characteristics that are different from one another. Characteristics possessed by humans are genuine which can be used as a differentiator between one individual and another, one of which is sound. Voice recognition is called speech recognition. In this study, it was developed as an individual voice recognition system using a combination of the Linear Predictive Coding (LPC) method of feature extraction and K-Nearest Neighbor (K-NN) classification in the speech recognition process. Testing is done by testing changes in several parameters, namely the LPC order value, the number of frames, the K value, and different distance methods. The results of the parameter combination test showed a fairly good presentation of 73.56321839% with the combination parameter or LPC 8, the number of frames 480, the value of K 5, with the distance method used by Chebychev.

scholarly journals Computational Approaches to Exploring Persian-Accented English

2015 ◽  
Vol 13 (1) ◽  
pp. 51-60
Author(s):  
Corey Miller
Keyword(s):  
Speech Recognition ◽  
Computational Approaches ◽  
Accented English ◽  
L2 Pronunciation

Methods involving phonetic speech recognition are discussed for detecting Persianaccented English. These methods offer promise for both the identification and mitigation of L2 pronunciation errors. Pronunciation errors, both segmental and suprasegmental, particular to Persian speakers of English are discussed.

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