A Mathematical Approach to Speech Enhancement for Speech Recognition and Speaker Identification Systems

2021 ◽  
pp. 647-660
Author(s):  
Rohun Nisa ◽  
Haweez Showkat ◽  
Asifa Baba
Keyword(s):  
Speech Recognition ◽  
Speech Enhancement ◽  
Speaker Identification ◽  
Mathematical Approach

Speech Enhancement Using End-to-End Speech Recognition Objectives

2019 ◽  
Author(s):  
Aswin Shanmugam Subramanian ◽  
Xiaofei Wang ◽  
Murali Karthick Baskar ◽  
Shinji Watanabe ◽  
Toru Taniguchi ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Speech Enhancement ◽  
End To End

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>

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