Application of sensor-based sound control principle in speech recognition technology

被引:0
|
作者
Wang, Xuejun [1 ]
机构
[1] Nanyang Vocat Coll Sci & Technol, Dengzhou 474150, Henan, Peoples R China
关键词
Speech recognition technology; Sensor; Sound control principle; DSP technology; IMPLEMENTATION; SYSTEM;
D O I
10.1007/s13198-023-01939-8
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The existing research in speech recognition theory, VLSI and computer basically meet the requirements of real-time signal processing, and has been widely used in management, communication and consumer goods industries. Speech signal, speech recognition has been more and more widely used. The terminal designed in this paper is a pure optical fiber speech sensor with pure optical structure, which can be used for speech signal acquisition and recovery and speech source location. In this paper, the principle of the system is considered, and the design and experimental verification of the system are completed. Based on the research foundation of hybrid optical fiber phi-OTDR sensor, the localization technology of speech sensor based on optical fiber vibration sensor is studied.The so-called voice control technology is essentially a technology that uses voice application technology to control or operate a mobile phone. The mobile phone displays a certain value and then performs a corresponding function. This article briefly introduces TI's DSP technology, and proposes a corresponding speech recognition system based on the established theory. The voice signal is converted by the analog-to-digital method, and the converted digital signal is sent to the DSP for processing and recognition, and the recognition result is output to the partial reconfiguration scheme implemented by the CPLD. The final focus is on the design of the signal processing module, the voice acquisition module, and the memory expansion. Module, CPLD control module, power supply module, etc.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Codebook Approach for Sensor-based Human Activity Recognition
    Shirahama, Kimiaki
    Koeping, Lukas
    Grzegorzek, Marcin
    UBICOMP'16 ADJUNCT: PROCEEDINGS OF THE 2016 ACM INTERNATIONAL JOINT CONFERENCE ON PERVASIVE AND UBIQUITOUS COMPUTING, 2016, : 197 - 200
  • [22] Deep learning for sensor-based activity recognition: A survey
    Wang, Jindong
    Chen, Yiqiang
    Hao, Shuji
    Peng, Xiaohui
    Hu, Lisha
    PATTERN RECOGNITION LETTERS, 2019, 119 : 3 - 11
  • [23] A Sensor-based Approach to Symptom Recognition for Autonomic Systems
    Li, Jeffery
    Martin, Patrick
    Powley, Wendy
    Wilson, Kirk
    Craddock, Chris
    ICAS: 2009 FIFTH INTERNATIONAL CONFERENCE ON AUTONOMIC AND AUTONOMOUS SYSTEMS, 2009, : 45 - +
  • [24] From action to activity: Sensor-based activity recognition
    Liu, Ye
    Nie, Liqiang
    Liu, Li
    Rosenblum, David S.
    NEUROCOMPUTING, 2016, 181 : 108 - 115
  • [25] Three-dimensional sensor-based face recognition
    Song, H
    Lee, S
    Kim, J
    Sohn, K
    APPLIED OPTICS, 2005, 44 (05) : 677 - 687
  • [26] A Survey: The Sensor-Based Method for Sign Language Recognition
    Yang, Tian
    Shen, Cong
    Wang, Xinyue
    Ma, Xiaoyu
    Ling, Chen
    PATTERN RECOGNITION AND COMPUTER VISION, PRCV 2023, PT VI, 2024, 14430 : 257 - 268
  • [27] Business model for sensor-based fall recognition systems
    Fachinger, Uwe
    Schoepke, Birte
    INFORMATICS FOR HEALTH & SOCIAL CARE, 2014, 39 (3-4): : 305 - 318
  • [28] ConvBoost: Boosting ConvNets for Sensor-based Activity Recognition
    Shao, Shuai
    Guan, Yu
    Zhai, Bing
    Missier, Paolo
    Plotz, Thomas
    PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT, 2023, 7 (02):
  • [29] Similarity Segmentation Approach for Sensor-Based Activity Recognition
    Baraka, AbdulRahman M. A.
    Noor, Mohd Halim Mohd
    IEEE SENSORS JOURNAL, 2023, 23 (17) : 19704 - 19716
  • [30] Wearable Sensor-Based Activity Recognition for Housekeeping Task
    Liu, Kai-Chun
    Yen, Chien-Yi
    Chang, Li-Han
    Hsieh, Chia-Yeh
    Chan, Chia-Tai
    2017 IEEE 14TH INTERNATIONAL CONFERENCE ON WEARABLE AND IMPLANTABLE BODY SENSOR NETWORKS (BSN), 2017, : 67 - 70