Study of the load balancing in the parallel training for automatic speech recognition

被引：0
|
作者：
Daoudi, E [1 ]
Manneback, P
Meziane, A
El Hadj, YOM
机构：
[1] Univ Mohammed I, Fac Sci, Oujda 60000, Morocco
[2] Fac Polytech Mons, B-7000 Mons, Belgium
来源：
EURO-PAR 2000 PARALLEL PROCESSING, PROCEEDINGS | 2000年 / 1900卷
关键词：
automatic speech recognition; Markovian modeling; parallel processing; load balancing;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
In this paper we propose a parallelization technique of the training phase for the automatic speech recognition using the Hidden Markov Models (HMMs), which improves the load balancing in the previous proposed parallel implementations [1]. This technique is based on an efficient distribution of the vocabulary on processors taking into account, not only the size of the vocabulary, but also the length of each word. In this manner the idle time will be reduced. The experimental results show that good performances can be obtained with this distribution.
引用
下载
收藏
页码：506 / 510
页数：5
相关论文
共 50 条
  • [21] A Parallel Interval Computation Model for Global Optimization with Automatic Load Balancing
    Wu, Yong
    Kumar, Arun
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2012, 27 (04) : 744 - 753
  • [22] A Study on Automatic Speech Recognition Toolkits
    Ganesh, D. Satya
    Sahu, Prasant Kumar
    2015 INTERNATIONAL CONFERENCE ON MICROWAVE, OPTICAL AND COMMUNICATION ENGINEERING (ICMOCE), 2015, : 365 - 368
  • [23] SELF-CRITICAL SEQUENCE TRAINING FOR AUTOMATIC SPEECH RECOGNITION
    Chen, Chen
    Hu, Yuchen
    Hou, Nana
    Qi, Xiaofeng
    Zou, Heqing
    Chng, Eng Siong
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 3688 - 3692
  • [24] JOINT NOISE ADAPTIVE TRAINING FOR ROBUST AUTOMATIC SPEECH RECOGNITION
    Narayanan, Arun
    Wang, DeLiang
    2014 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2014,
  • [25] PaSCoNT - Parallel Speech Corpus of Northern-central Thai for automatic speech recognition
    Taerungruang, Supawat
    Taninpong, Phimphaka
    Chunwijitra, Vataya
    Thatphithakkul, Sumonmas
    Kasuriya, Sawit
    Inthanon, Viroj
    Paksaranuwat, Pawat
    Thumronglaohapun, Salinee
    Nakharutai, Nawapon
    Inkeaw, Papangkorn
    Bootkrajang, Jakramate
    COMPUTER SPEECH AND LANGUAGE, 2025, 89
  • [26] FedNST: Federated Noisy Student Training for Automatic Speech Recognition
    Mehmood, Haaris
    Dobrowolska, Agnieszka
    Saravanan, Karthikeyan
    Ozay, Mete
    INTERSPEECH 2022, 2022, : 1001 - 1005
  • [27] Parallel implementation of Artificial Neural Network training for speech recognition
    Scanzio, Stefano
    Cumani, Sandro
    Gemello, Roberto
    Mana, Franco
    Laface, P.
    PATTERN RECOGNITION LETTERS, 2010, 31 (11) : 1302 - 1309
  • [28] SENTIMENT-AWARE AUTOMATIC SPEECH RECOGNITION PRE-TRAINING FOR ENHANCED SPEECH EMOTION RECOGNITION
    Ghriss, Ayoub
    Yang, Bo
    Rozgic, Viktor
    Shriberg, Elizabeth
    Wang, Chao
    2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 7347 - 7351
  • [29] Load balancing for parallel forwarding
    Shi, WG
    MacGregor, MH
    Gburzynski, P
    IEEE-ACM TRANSACTIONS ON NETWORKING, 2005, 13 (04) : 790 - 801
  • [30] Comparative study of automatic speech recognition techniques
    Cutajar, Michelle
    Gatt, Edward
    Grech, Ivan
    Casha, Owen
    Micallef, Joseph
    IET SIGNAL PROCESSING, 2013, 7 (01) : 25 - 46
12345