Significance of Automatic Detection of Vowel Regions for Automatic Shout Detection in Continuous Speech

被引:0
|
作者
Mittal, Vinay Kumar [1 ]
Vuppala, Anil Kumar [2 ]
机构
[1] Indian Inst Informat Technol Chittoor, Sri City, Andhra Pradesh, India
[2] Int Inst Informat Technol, Hyderabad, Telangana, India
关键词
automatic vowel detection; shout detection; vowel onset point; zero-frequency filtering; dominant frequency; ONSET POINT DETECTION;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Automatic detection of shout prosody in continuous speech signal involves examining changes in its production characteristics. Our recent study of electroglottograph signals highlighted that significant changes occur in the glottal excitation source characteristics during production of shouted speech, especially in the vowel contexts. But the differences between normal and shouted speech, in the production features derived over utterances or word segments, may be masked sometimes by pauses or unvoiced regions related variations. Also, for such a real-time system, these vowel regions need to be found automatically. In this paper, changes in the shout production features are examined in the automatically detected vowel regions. Production of a vowel involves periodic impulse-like excitation and relatively high signal energy. Hence, the knowledge of epochs using zero-frequency filtering, and accurate vowel onset points can be used for detecting these regions. Changes in two excitation source features, the instantaneous fundamental frequency and strength of excitation, and in a vocal tract filter feature the dominant frequency, are examined for five steady vowel regions. Larger changes in these distinguishing features are observed in the automatically found vowel regions, than in word segments. This approach can help improving the systems for automatic detection of shout regions in continuous speech, and in paralinguistic applications that involve detection of prosody or emotions.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Automatic detection of vocalized hesitations in Russian speech
    Verkhodanova, Vasilisa O.
    Shapranov, Vladimir V.
    Kipyatkova, Irina S.
    Karpov, Alexey A.
    VOPROSY YAZYKOZNANIYA, 2018, (06): : 104 - 118
  • [32] Automatic detection of diseased regions in knee cartilage
    Qazi, Arish A.
    Dam, Erik B.
    Olsen, Ole F.
    Nielsen, Mads
    Christiansen, Claus
    MEDICAL IMAGING 2007: IMAGE PROCESSING, PTS 1-3, 2007, 6512
  • [33] Automatic detection of active regions on solar images
    Benkhalil, A
    Zharkova, V
    Ipson, S
    Zharkov, S
    KNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 3, PROCEEDINGS, 2004, 3215 : 460 - 466
  • [34] Automatic detection of Bounded Weak Echo Regions
    Lakshmanan, V
    Witt, A
    28TH CONFERENCE ON RADAR METEOROLOGY, 1997, : 366 - 367
  • [35] SIGNIFICANCE TESTING IN AUTOMATIC INTERACTION DETECTION (AID)
    KASS, GV
    THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS, 1975, 24 (02): : 178 - 189
  • [36] Automatic Detection of Vowel Pronunciation Errors Using Multiple Information Sources
    van Doremalen, Joost
    Cucchiarini, Catia
    Strik, Helmer
    2009 IEEE WORKSHOP ON AUTOMATIC SPEECH RECOGNITION & UNDERSTANDING (ASRU 2009), 2009, : 580 - 585
  • [37] HOW AUTOMATIC IS AUTOMATIC DETECTION
    HOFFMAN, JE
    NELSON, B
    LAUBACH, M
    BULLETIN OF THE PSYCHONOMIC SOCIETY, 1979, 14 (04) : 262 - 262
  • [38] AUTOMATIC DETECTION OF VOICE ONSET TIME IN DYSARTHRIC SPEECH
    Novotny, Michal
    Pospisil, Jakub
    Cmejla, Roman
    Rusz, Jan
    2015 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING (ICASSP), 2015, : 4340 - 4344
  • [39] Automatic detection of stridence in speech using the auditory model
    Bilibajkic, Ruzica
    Saric, Zoran
    Jovicic, Slobodan T.
    Punisic, Silvana
    Subotic, Misko
    COMPUTER SPEECH AND LANGUAGE, 2016, 36 : 122 - 135
  • [40] Automatic Detection of High Vocal Effort in Telephone Speech
    Pohjalainen, Jouni
    Raitio, Tuomo
    Pulakka, Hannu
    Alku, Paavo
    13TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2012 (INTERSPEECH 2012), VOLS 1-3, 2012, : 690 - 693