scholarly journals Langkah Praktis Membangun Sistem Pengenalan Suara dengan HTK

2019 ◽  
Vol 2 (2) ◽  
pp. 149-153
Author(s):  
Zulkarnaen Hatala
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Error Rate ◽  
Principle Component Analysis ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Word Error Rate ◽  
Bahasa Indonesia

Dipaparkan prosedur untuk mengembangkan Sistem Pengenalan Suara otomatis, Automatic Speech Recognition System (ASR) untuk kasus online recognition. Prosedur ini  secara cepat dan efisien membangun ASR menggunakan Hidden Markov Toolkit (HTK). Langkah-langkah praktis ini dipaparkan secara jelas untuk mengimplementasikan ASR dengan daftar kata sedikit (Small Vocabulary) dalam contoh kasus pengenalan digit Bahasa Indonesia. Dijelaskan beberapa teknik meningkatkan performansi seperti cara mengatasi noise, pengejaan ganda dan penerapan Principle Component Analysis. Hasil akhir berupa Word Error Rate

scholarly journals Speech Recognition Implementation

2019 ◽  
Vol 8 (9S4) ◽  
pp. 111-116
Keyword(s):  
Speech Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Automatic Speech Recognition ◽  
Hidden Markov ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System ◽  
Main Challenge ◽  
Short Time

This paper presents a brief review on Automatic Speech Recognition and provide a technical understanding of ASR system. The objective of this review paper is to elaborate one of the best techniques in the field of speech recognition that is hidden Markov model. Hidden Markov model is very popular technique for speech recognition because speech signal is more like piecewise stationary or short time stationary signal and these models can be trained easily and they are computationally feasible. So, this paper gives a proper implementation of hidden Markov model. After so many years of research, the main challenge in speech recognition field is accuracy. The speech recognition system includes feature extraction, building word template, comparing word and selecting the best with maximum likelihood. Hence, this paper will give a great contribution for understanding the concepts of Automatic Speech Recognition system and hidden Markov model.

scholarly journals BART Based Semantic Correction for Mandarin Automatic Speech Recognition System

2021 ◽  
Author(s):  
Yun Zhao ◽  
Xuerui Yang ◽  
Jinchao Wang ◽  
Yongyu Gao ◽  
Chao Yan ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System

scholarly journals Development of the Arabic Loria Automatic Speech Recognition system (ALASR) and its evaluation for Algerian dialect

2017 ◽  
Vol 117 ◽  
pp. 81-88 ◽  
Author(s):  
Mohamed Amine Menacer ◽  
Odile Mella ◽  
Dominique Fohr ◽  
Denis Jouvet ◽  
David Langlois ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Automatic Speech Recognition System
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