Recognition and Classification of Pauses in Stuttered Speech using Acoustic Features

被引:0
|
作者
Afroz, Fathima [1 ]
Koolagudi, Shashidhar G. [2 ]
机构
[1] JSS Acad Tech Educ Bangalore, Dept Informat Sci & Engn, JSSATE B Campus Dr Vishnuvardan Rd, Bengaluru 560060, Karnataka, India
[2] NITK, Dept Comp Sci & Engn, NH 66, Mangaluru 575025, Karnataka, India
关键词
Terms Acoustic Features; Blind segmentation; Intermorphic pauses; Intra-morphic pauses; Stuttered Speech;
D O I
10.1109/spin.2019.8711569
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
Pauses plays an essential role in speech activities. Normally it helps the listener by creating a time and space to decode and interpret the message of a speaker. But in case of stuttering pauses disturbs the normal flow of speech. The uncontrolled, frequent and unplanned occurance of pasuses leads to slow speaking rate, results in broken words and increases the severity level of stuttering. Hence pauses and stuttering has a close relationship. Pauses are considered as one of the important pattern in diagnoisis and treatment of stuttering. In this work, an attempt has been made for the identification of inaudible (Silent or Unfilled) pauses from stuttered speech. The attributes like duration, frequency, position and distribution of pauses during speech tasks are measured and quantified. UCLASS stuttered speech corpus is considered for the analysis. Automatic blind segmentation approach is adopted to segment the speech signal into voice and unvoiced regions using dynamic threshold set based on energy and zero crossing rate (ZCR). 4th formant frequencies are analysed to identify intra-morphic (unfilled) pauses present within voiced regions. The duratiion of intra-morphic pauses are analysed for stuttred speech and normal speech. It is observed that the duration of normal intramorphic pause ranges from 150 ms-250 ms and inter-morphic pauses are <=250 ms and short pause have duration ranges from 50 ms-150 ms. Whereas in stuttering short intra-morphic pauses ranges from 10 ms to 50 ms, long pauses ranges from 250 ms to 1 or 2 seconds. Segmentation of the intra-morphic pauses is observed to acheive an accuracy of 98%. Results are compared and validated with manual method.
引用
收藏
页码:921 / 926
页数:6
相关论文
共 50 条
  • [21] SPEECH EMOTION RECOGNITION WITH ACOUSTIC AND LEXICAL FEATURES
    Jin, Qin
    Li, Chengxin
    Chen, Shizhe
    Wu, Huimin
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 4749 - 4753
  • [22] Novel acoustic features for speech emotion recognition
    Roh Yong-Wan
    Kim Dong-Ju
    Lee Woo-Seok
    Hong Kwang-Seok
    SCIENCE IN CHINA SERIES E-TECHNOLOGICAL SCIENCES, 2009, 52 (07): : 1838 - 1848
  • [23] A Study of Acoustic Features for the Classification of Depressed Speech
    Lopez-Otero, Paula
    Docio-Fernandez, Laura
    Garcia-Mateo, Carmen
    2014 37TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2014, : 1331 - 1335
  • [24] Acoustic Features for Classification Based Speech Separation
    Wang, Yuxuan
    Han, Kun
    Wang, DeLiang
    13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, 2012, : 1530 - 1533
  • [25] Speech Recognition and Acoustic Features in Combined Electric and Acoustic Stimulation
    Yoon, Yang-Soo
    Li, Yongxin
    Fu, Qian-Jie
    JOURNAL OF SPEECH LANGUAGE AND HEARING RESEARCH, 2012, 55 (01): : 105 - 124
  • [26] Speech emotion recognition: Features and classification models
    Chen, Lijiang
    Mao, Xia
    Xue, Yuli
    Cheng, Lee Lung
    DIGITAL SIGNAL PROCESSING, 2012, 22 (06) : 1154 - 1160
  • [27] WFST-BASED STRUCTURAL CLASSIFICATION INTEGRATING DNN ACOUSTIC FEATURES AND RNN LANGUAGE FEATURES FOR SPEECH RECOGNITION
    Quoc Truong Do
    Nakamura, Satoshi
    Delcroix, Marc
    Hori, Takaaki
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 4959 - 4963
  • [28] Filled pauses in multilingual speech: an acoustic analysis
    Spreafico, Lorenzo
    LINGUISTICA E FILOLOGIA, 2016, (36): : 99 - 116
  • [29] Machine learning techniques for speech emotion recognition using paralinguistic acoustic features
    Jha T.
    Kavya R.
    Christopher J.
    Arunachalam V.
    International Journal of Speech Technology, 2022, 25 (03): : 707 - 725
  • [30] ACOUSTIC ANALYSIS AND PERCEPTION OF VOWELS IN CHILDRENS AND TEENAGERS STUTTERED SPEECH
    HOWELL, P
    WILLIAMS, M
    JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA, 1992, 91 (03): : 1697 - 1706