Features extraction for the automatic detection of ALS disease from acoustic speech signals

被引:0
|
作者
Vashkevich, Maxim [1 ]
Azarov, Elias [1 ]
Petrovsky, Alexander [1 ]
Rushkevich, Yuliya [2 ]
机构
[1] Belarusian State Univ Informat & Radioelect, Dept Comp Engn, 6 PBrovky str, Minsk 220013, BELARUS
[2] Republican Res & Clin Ctr Neurol & Neurosurg, Minsk, BELARUS
关键词
speech analysis; formants; ALS;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
The paper presents a features for detection of pathological changes in acoustic speech signal for the diagnosis of the bulbar form of Amyotrophic Lateral Sclerosis (ALS). We collected records of the running speech test from 48 people, 26 with ALS. The proposed features are based on joint analysis of different vowels. Harmonic structure of the vowels are also taken into consideration. We also presenting the rationale of vowels selection for calculation of the proposed features. Applying this features to classification task using linear discriminant analysis (LDA) lead to overall correct classification performance of 88.0%.
引用
收藏
页码:321 / 326
页数:6
相关论文
共 50 条
  • [31] AUTOMATIC DETECTION OF EPILEPTIC SEIZURE BY EXTRACTING STATISTICALS FEATURES FROM EEG SIGNALS
    Issaka, Mahamat Ali
    Dabye, Ali S.
    Gueye, Lamine
    JP JOURNAL OF BIOSTATISTICS, 2015, 12 (01) : 15 - 31
  • [32] Automatic Extraction of Disease-specific Features from Doppler Images
    Negahdar, Mohammadreza
    Moradi, Mehdi
    Parajuli, Nripesh
    Syeda-Mahmood, Tanveer
    MEDICAL IMAGING 2017: COMPUTER-AIDED DIAGNOSIS, 2017, 10134
  • [33] CUEX: An algorithm for automatic extraction of expressive tone parameters in music performance from acoustic signals
    Friberg, Anders
    Schoonderwaldt, Erwin
    Juslin, Patrik N.
    ACTA ACUSTICA UNITED WITH ACUSTICA, 2007, 93 (03) : 411 - 420
  • [34] Automatic Detection of Obstructive Sleep Apnea Using Speech Signals
    Goldshtein, Evgenia
    Tarasiuk, Ariel
    Zigel, Yaniv
    IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2011, 58 (05) : 1373 - 1382
  • [35] Improvement of automatic speech recognition systems via nonlinear dynamical features evaluated from the recurrence plot of speech signals
    Firooz, Shabnam Gholamdokht
    Almasganj, Farshad
    Shekofteh, Yasser
    COMPUTERS & ELECTRICAL ENGINEERING, 2017, 58 : 215 - 226
  • [36] Automatic Speech Intelligibility Detection for Speakers with Speech Impairments: The Identification of Significant Speech Features
    Rosdi, Fadhilah
    Mustafa, Mumtaz Begum
    Salim, Siti Salwah
    Zin, Nor Azan Mat
    SAINS MALAYSIANA, 2019, 48 (12): : 2737 - 2747
  • [37] SUBMODULAR DATA SELECTION WITH ACOUSTIC AND PHONETIC FEATURES FOR AUTOMATIC SPEECH RECOGNITION
    Ni, Chongjia
    Wang, Lei
    Liu, Haibo
    Leung, Cheung-Chi
    Lu, Li
    Ma, Bin
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 4629 - 4633
  • [38] Automatic Recognition of Spontaneous Emotions in Speech Using Acoustic and Lexical Features
    Truong, Khict P.
    Raaijmakers, Stephan
    MACHINE LEARNING FOR MULTIMODAL INTERACTION, PROCEEDINGS, 2008, 5237 : 161 - +
  • [39] Noise-Robust Algorithm of Speech Features Extraction for Automatic Speech Recognition System
    Yakhnev, A. N.
    Pisarev, A. S.
    PROCEEDINGS OF THE XIX IEEE INTERNATIONAL CONFERENCE ON SOFT COMPUTING AND MEASUREMENTS (SCM 2016), 2016, : 206 - 208
  • [40] Towards Automatic Detection of Amyotrophic Lateral Sclerosis from Speech Acoustic and Articulatory Samples
    Wang, Jun
    Kothalkar, Prasanna V.
    Cao, Beiming
    Heitzman, Daragh
    17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5: UNDERSTANDING SPEECH PROCESSING IN HUMANS AND MACHINES, 2016, : 1195 - 1199