Detection of Attributes for Bengali Phoneme in Continuous Speech using Deep Neural Network

被引：0
|
作者：
Bhowmik, Tanmay [1 ]
Mukherjee, Sankar [2 ]
Das Mandal, Shyamal Kumar [1 ]
机构：
[1] IIT Kharagpur, Ctr Educ Technol, Kharagpur, W Bengal, India
[2] Aix Marseille Univ, CNRS, Lab Parole & Langage, Marseille, France
来源：
2ND INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING AND INTEGRATED NETWORKS (SPIN) 2015 | 2015年
关键词：
speech attribute; place of articulation; manner of articulation; deep neural network; deep belief network;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Hidden Markov Model (HMM) has contributed greatly in the area of speech recognition during last two decades. However, in recent days, detection-based, bottom-up speech recognition techniques achieve high success rate. In this detection-based, bottom-up approach of speech recognition, first step is detection of speech attributes like place and manner of articulation of the phonemes. This paper describes about the detection of attributes which leads to identification of place and manner of articulation of Bengali phonemes using Deep Neural Network (DNN).
引用
收藏
页码：103 / 108
页数：6
相关论文
共 50 条
  • [41] Deep Neural Network Frontend for Continuous EMG-based Speech Recognition
    Wand, Michael
    Schmidhuber, Jurgen
    17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES, 2016, : 3032 - 3036
  • [42] Detection of phonological features in continuous speech using neural networks
    King, S
    Taylor, P
    COMPUTER SPEECH AND LANGUAGE, 2000, 14 (04): : 333 - 353
  • [43] Deep Neural Network Architectures for Speech Deception Detection: A Brief Survey
    Herchonvicz, Andrey Lucas
    de Santiago, Rafael
    PROGRESS IN ARTIFICIAL INTELLIGENCE (EPIA 2021), 2021, 12981 : 301 - 312
  • [44] A Study on Speech Emotion Recognition Using a Deep Neural Network
    Lee, Kyong Hee
    Choi, Hyun Kyun
    Jang, Byung Tae
    Kim, Do Hyun
    2019 10TH INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC): ICT CONVERGENCE LEADING THE AUTONOMOUS FUTURE, 2019, : 1162 - 1165
  • [45] Single Channel Speech Separation Using Deep Neural Network
    Chen, Linlin
    Ma, Xiaohong
    Ding, Shuxue
    ADVANCES IN NEURAL NETWORKS, PT I, 2017, 10261 : 285 - 292
  • [46] Improved Speech Emotion Classification Using Deep Neural Network
    Mariwan Hama Saeed
    Circuits, Systems, and Signal Processing, 2023, 42 : 7357 - 7376
  • [47] Improved Speech Emotion Classification Using Deep Neural Network
    Saeed, Mariwan Hama
    CIRCUITS SYSTEMS AND SIGNAL PROCESSING, 2023, 42 (12) : 7357 - 7376
  • [48] Dari Speech Classification Using Deep Convolutional Neural Network
    Dawodi, Mursal
    Baktash, Jawid Ahamd
    Wada, Tomohisa
    Alam, Najwa
    Joya, Mohammad Zarif
    2020 IEEE INTERNATIONAL IOT, ELECTRONICS AND MECHATRONICS CONFERENCE (IEMTRONICS 2020), 2020, : 110 - 113
  • [49] Fricative Phoneme Detection Using Deep Neural Networks and its Comparison to Traditional Methods
    Yurt, Metehan
    Kantharaju, Pavan
    Disch, Sascha
    Niedermeier, Andreas
    Escalante-B, Alberto N.
    Morgenshtern, Veniamin, I
    INTERSPEECH 2021, 2021, : 51 - 55
  • [50] Deep belief networks for phoneme recognition in continuous Tamil speech-an analysis
    Raguram, Laxmi Sree Baskaran
    Shanmugam, Vijaya Madhaya
    TRAITEMENT DU SIGNAL, 2017, 34 (3-4) : 137 - 151
12345