Automatic Speech Recognition for Thai Sentence based on MFCC and CNNs

被引:4
|
作者
Sukvichai, Kanjanapan [1 ]
Utintu, Chaitat [1 ]
Muknumporn, Warayut [1 ]
机构
[1] Kasetsart Univ, Dept Elect Engn, Bangkok, Thailand
关键词
Thai ASR; MFCC; YOLO; CNNs;
D O I
10.1109/ICA-SYMP50206.2021.9358451
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
An automatic speech recognition (ASR) is more important, especially in the Coronavirus outbreak. ASR for Thai sentence was proposed based on MFCC and CNNs in this research. The MFCC features image created from the Thai speech procedure is explained. The MFCC image is treated as a normal image. Object detection techniques based on CNNs can be used to detect Thai words in the frequency image. You Only Look Once (YOLO) is selected as the word localizer and classifier due to its performance and accuracy. The airport service scenario is explored in this research in order to obtain the performance of the proposed system. The airport information system is selected for the experiments. Speeches were collected from 60 participants with 50% males and 50% females. Speech images are constructed based on MFCC and labeled for specific Thai keywords. The YOLOv3 and Tiny YOLOv3 were trained and the performance was evaluated. Clearly, Tiny YOLOv3 network is good enough for this experiment. New speech data provided from new 20 participants were used to test the proposed system. Resulting in the proposed ASR system based on MFCC and CNNs has a good performance in both speed and accuracy.
引用
收藏
页码:108 / 111
页数:4
相关论文
共 50 条
  • [1] Thai automatic speech recognition
    Suebvisai, S
    Charoenpomsawat, P
    Black, A
    Woszczyna, M
    Schultz, T
    2005 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS 1-5: SPEECH PROCESSING, 2005, : 857 - 860
  • [2] Speech Emotion Recognition Based on Improved MFCC
    Wang, Yan
    Hu, Weiping
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND APPLICATION ENGINEERING (CSAE2018), 2018,
  • [3] An analysis on LPC, RASTA and MFCC techniques in Automatic Speech Recognition System
    Gupta, Kartiki
    Gupta, Divya
    2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 2016, : 493 - 497
  • [4] Design of an Automatic Speaker Recognition System Based on Adapted MFCC and GMM Methods for Arabic Speech
    Tazi, El Bachir
    Benabbou, Abderrahim
    Harti, Mostafa
    INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2010, 10 (01): : 45 - 50
  • [5] Automatic Speech Recognition of Isolated Words in Hindi Language using MFCC
    Patil, U. G.
    Shirbahadurkar, S. D.
    Paithane, A. N.
    2016 INTERNATIONAL CONFERENCE ON COMPUTING, ANALYTICS AND SECURITY TRENDS (CAST), 2016, : 433 - 438
  • [6] Thai spelling analysis for automatic spelling speech recognition
    Pisarn, Chutima
    Theeramunkong, Thanaruk
    INFORMATION SCIENCES, 2008, 178 (01) : 122 - 136
  • [7] Speech Based Human Emotion Recognition Using MFCC
    Likitha, M. S.
    Gupta, Raksha R.
    Hasitha, K.
    Raju, A. Upendra
    2017 2ND IEEE INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, SIGNAL PROCESSING AND NETWORKING (WISPNET), 2017, : 2257 - 2260
  • [8] The speech recognition system based on bark wavelet MFCC
    Zhang, Xue-ying
    Bai, Jing
    Liang, Wu-zhou
    2006 8TH INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING, VOLS 1-4, 2006, : 780 - +
  • [9] Enriching speech recognition with automatic detection of sentence boundaries and disfluencies
    Liu, Yang
    Shriberg, Elizabeth
    Stolcke, Andreas
    Hillard, Dustin
    Ostendorf, Mari
    Harper, Mary
    IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2006, 14 (05): : 1526 - 1540
  • [10] MFCC-based Recurrent Neural Network for automatic clinical depression recognition and assessment from speech
    Rejaibi, Emna
    Komaty, Ali
    Meriaudeau, Fabrice
    Agrebi, Said
    Othmani, Alice
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2022, 71