• 말소리와 음성과학
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• 제6권3호
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• pp.63-72
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• 2014

The current study assessed the utility of acoustic analyses the most commonly used in routine clinical voice assessment including perturbation, nonlinear dynamic analysis, and Spectral/Cepstrum analysis based on signal typing of dysphonic voices and investigated their applicability of clinical acoustic analysis methods. A total of 70 dysphonic voice samples were classified with signal typing using narrowband spectrogram. Traditional parameters of %jitter, %shimmer, and signal-to-noise ratio were calculated for the signals using TF32 and correlation dimension(D2) of nonlinear dynamic parameter and spectral/cepstral measures including mean CPP, CPP_sd, CPPf0, CPPf0_sd, L/H ratio, and L/H ratio_sd were also calculated with ADSV(Analysis of Dysphonia in Speech and VoiceTM). Auditory perceptual analysis was performed by two blinded speech-language pathologists with GRBAS. The results showed that nearly periodic Type 1 signals were all functional dysphonia and Type 4 signals were comprised of neurogenic and organic voice disorders. Only Type 1 voice signals were reliable for perturbation analysis in this study. Significant signal typing-related differences were found in all acoustic and auditory-perceptual measures. SNR, CPP, L/H ratio values for Type 4 were significantly lower than those of other voice signals and significant higher %jitter, %shimmer were observed in Type 4 voice signals(p<.001). Additionally, with increase of signal type, D2 values significantly increased and more complex and nonlinear patterns were represented. Nevertheless, voice signals with highly noise component associated with breathiness were not able to obtain D2. In particular, CPP, was highly sensitive with voice quality 'G', 'R', 'B' than any other acoustic measures. Thus, Spectral and cepstral analyses may be applied for more severe dysphonic voices such as Type 4 signals and CPP can be more accurate and predictive acoustic marker in measuring voice quality and severity in dysphonia.

• 한국음향학회지
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• 제20권8호
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• pp.24-30
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• 2001

본 논문에서는 발음표기가 주어진 상황에서 음성 신호의 자동 음소 분할에 관한 것이며 음소의 경계를 음소 음향학적인 변화특성에 따라 3가지 형태로 분류하여 각각에 적합한 분할 알고리즘을 개발하였다. 형태 1은 묵음·유성음·무성음간의 분할이며 히스토그램분석으로 구한 문턱 값으로 초기 분할 후, 웨이블릿 계수의 SVF (Spectral Variation Function)를 이용하여 분할하였다. 형태 2는 연속적인 모음의 분할이며 각 모음변화특성을 템플릿으로 구성하여 분할에 활용하였다. 형태 3은 모음과 유성자음 혹은 유성화 자음의 분할이며 특성주파수대역의 진폭변화를 이용하여 후보구간을 정한 후, 캡스트럼 계수의 SVF를 이용하여 최종적인 분할을 수행하였다. 본 실험에서는 분할 성능을 테스트하기 위하여 한국어 PBWSpeech DB에서 342개의 단어를 자동으로 분할한 후, 수작업으로 분할한 결과와 비교하였다. 전체적인 자동 분할 성능은 20 msec내에서 81.5%의 분할성능을 보였다.