A NEURAL NETWORK-BASED LEARNING-SYSTEM FOR SPEECH PROCESSING

被引：1
|
作者：
PALAKAL, MJ
ZORAN, MJ
机构：
[1] Indiana University-Purdue University at Indianapolis, Indianapolis, IN
来源：
EXPERT SYSTEMS WITH APPLICATIONS | 1991年 / 2卷 / 01期
基金：
美国国家科学基金会;
关键词：
D O I：
10.1016/0957-4174(91)90134-Z
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Learning is an essential part of any intelligent system and it is an inherent property in Artificial Neural Network (ANN) models. Recently, artificial neural network models have begun to emerge as powerful tools for learning, and for recognizing patterns with great variability similar to speech patterns. In the past, expert systems proved to be the most promising tools to handle highly variable data. During previous work in this area, we have developed a speech recognition system that uses certain expert system principles. Here, we describe an unsupervised learning method that is used to learn speech signal properties from a speech image such as a spectrogram. © 1991.
引用
收藏
页码：59 / 71
页数：13
相关论文
共 50 条
  • [1] Recurrent Neural Network-Based Dictionary Learning for Compressive Speech Sensing
    Ji, Yunyun
    Zhu, Wei-Ping
    Champagne, Benoit
    [J]. CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2019, 38 (08) : 3616 - 3643
  • [2] Recurrent Neural Network-Based Dictionary Learning for Compressive Speech Sensing
    Yunyun Ji
    Wei-Ping Zhu
    Benoit Champagne
    [J]. Circuits, Systems, and Signal Processing, 2019, 38 : 3616 - 3643
  • [3] Neural network-based blended ensemble learning for speech emotion recognition
    Bhanusree Yalamanchili
    Srinivas Kumar Samayamantula
    Koteswara Rao Anne
    [J]. Multidimensional Systems and Signal Processing, 2022, 33 : 1323 - 1348
  • [4] Neural network-based blended ensemble learning for speech emotion recognition
    Yalamanchili, Bhanusree
    Samayamantula, Srinivas Kumar
    Anne, Koteswara Rao
    [J]. MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING, 2022, 33 (04) : 1323 - 1348
  • [5] Neural network-based residue number signal processing system
    Yao, Tianren
    [J]. High Technology Letters, 1995, 5 (07):
  • [6] Style Transplantation in Neural Network-based Speech Synthesis
    Suzic, Sinisa B.
    Delic, Tijana, V
    Pekar, Darko J.
    Delic, Vlado D.
    Secujski, Milan S.
    [J]. ACTA POLYTECHNICA HUNGARICA, 2019, 16 (06) : 171 - 189
  • [7] Neural network-based artificial bandwidth expansion of speech
    Kontio, Juho
    Laaksonen, Laura
    Alku, Paavo
    [J]. IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2007, 15 (03): : 873 - 881
  • [8] Integrating Uncertainty Into Neural Network-Based Speech Enhancement
    Fang, Huajian
    Becker, Dennis
    Wermter, Stefan
    Gerkmann, Timo
    [J]. IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, 2023, 31 : 1587 - 1600
  • [9] The Asian Network-based Speech-to-Speech Translation System
    Sakti, Sakriani
    Kimura, Noriyuki
    Paul, Michael
    Hori, Chiori
    Sumita, Eiichiro
    Nakamura, Satoshi
    Park, Jun
    Wutiwiwatchai, Chai
    Xu, Bo
    Riza, Hammam
    Arora, Karunesh
    Luong, Chi Mai
    Li, Haizhou
    [J]. 2009 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION & UNDERSTANDING (ASRU 2009), 2009, : 507 - +
  • [10] Deep Neural Network-based Speech Separation Combining with MVDR Beamformer for Automatic Speech Recognition System
    Lee, Bong-Ki
    Jeong, Jaewoong
    [J]. 2019 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), 2019,
12345