• Title/Summary/Keyword: Degree of Voice Breaks

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Automatic severity classification of dysarthria using voice quality, prosody, and pronunciation features (음질, 운율, 발음 특징을 이용한 마비말장애 중증도 자동 분류)

  • Yeo, Eun Jung;Kim, Sunhee;Chung, Minhwa
    • Phonetics and Speech Sciences
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    • v.13 no.2
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    • pp.57-66
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    • 2021
  • This study focuses on the issue of automatic severity classification of dysarthric speakers based on speech intelligibility. Speech intelligibility is a complex measure that is affected by the features of multiple speech dimensions. However, most previous studies are restricted to using features from a single speech dimension. To effectively capture the characteristics of the speech disorder, we extracted features of multiple speech dimensions: voice quality, prosody, and pronunciation. Voice quality consists of jitter, shimmer, Harmonic to Noise Ratio (HNR), number of voice breaks, and degree of voice breaks. Prosody includes speech rate (total duration, speech duration, speaking rate, articulation rate), pitch (F0 mean/std/min/max/med/25quartile/75 quartile), and rhythm (%V, deltas, Varcos, rPVIs, nPVIs). Pronunciation contains Percentage of Correct Phonemes (Percentage of Correct Consonants/Vowels/Total phonemes) and degree of vowel distortion (Vowel Space Area, Formant Centralized Ratio, Vowel Articulatory Index, F2-Ratio). Experiments were conducted using various feature combinations. The experimental results indicate that using features from all three speech dimensions gives the best result, with a 80.15 F1-score, compared to using features from just one or two speech dimensions. The result implies voice quality, prosody, and pronunciation features should all be considered in automatic severity classification of dysarthria.

A Study on the Acoustic Characteristics of the Pansori by Voice Signals Analysis (음성신호 분석에 의한 판소리의 음성학적 특징 연구)

  • Kim, HyunSook
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.14 no.7
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    • pp.3218-3222
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    • 2013
  • Pansori is our traditional vocal sound, originality and excellence in the art of conversation, gesture general became a globally recognized world intangible heritage. Especially, Pansori as shrews and humorous representation of audience participation with a high degree of artistic value and enjoy the arts throughout all layers to be responsible for the social integration of functions is evaluated. Therefore, in this paper, Pansori five yard target speech signal analysis techniques applied to analyze the Pansori acoustic features of a representation of a society and era correlation extraction studies were performed. Pansori on the five yard spectrogram, pitch, stability and strength analysis for this experiment. Pansori through experimental results Comical story while keeping the audience focused and interested to better reflect the characteristics of energy for the wave of voice and vocal cord tremor change the width of a large, stable and voice with a loud voice, that expresses were analyzed.

An Analysis of Preference for Korean Pop Music By Applying Acoustic Signal Analysis Techniques (음향신호분석 기술을 적용한 한국가요의 시대별 선호도 분석)

  • Cho, Dong-Uk;Kim, Bong-Hyun
    • The KIPS Transactions:PartD
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    • v.19D no.3
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    • pp.211-220
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    • 2012
  • Recently K-Pop gained worldwide sensational popularity, no longer limited to the domestic pop music scene. One of the main causes can be that K-Pop mostly are "Hook Song" which has the "hook effect": a certain melody or/and rhythm is repeated up to 70 times in one song so that it hooks the ear of the listener. Also, visual effects by K-Pop dance group are supposed to contribute to gaining the popularity. In this paper, we propose a method which traces the changes of preference for Korean pop music according to the passing of time and investigates the causes using acoustic signal analysis. For this, experiments in acoustic signal analysis are performed on Korean pop music of from popular female singers in 1960s to those as of this date. Experimental results by applying acoustic signal processing techniques show that the periods discrimination is possible based on scientific evidences. Also, quantitative, objective and numerical data based on acoustic signal processing techniques are extracted compared with the pre-existing methods such as subjective and statistical data.