• Journal of the Korea Society of Computer and Information
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• v.5 no.3
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• pp.51-57
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• 2000

This paper proposes speaker recognition algorithm which includes both the pitch and the fuzzy. This study proposes a pitch detection method for the peak and valley pitch detection function by means of comparing spectra which utilizes the transform characteristics between time and frequency. It measures the similarity to the original spectrum while arbitrarily varying the period in the time domain. It heavily weights the error due to the changing characteristics of the phonemes, while it is strong against noise. In this paper, makes reference pattern using membership function and performs vocal track recognition of common character using fuzzy pattern matching in order to include time variation width for non-linear utterance time.

• Journal of Internet Computing and Services
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• v.19 no.6
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• pp.63-72
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• 2018

The human voice is one of the easiest methods for the information transmission between human beings. The characteristics of voice can vary from person to person and include the speed of speech, the form and function of the vocal organ, the pitch tone, speech habits, and gender. The human voice is a key element of human communication. In the days of the Fourth Industrial Revolution, voices are also a major means of communication between humans and humans, between humans and machines, machines and machines. And for that reason, people are trying to communicate their intentions to others clearly. And in the process, it contains various additional information along with the linguistic information. The Information such as emotional status, health status, part of trust, presence of a lie, change due to drinking, etc. These linguistic and non-linguistic information can be used as a device for evaluating the individual's credit worthiness by appearing in various parameters through voice analysis. Especially, it can be obtained by analyzing the relationship between the characteristics of the fundamental frequency(basic tonality) of the vocal cords, and the characteristics of the resonance frequency of the vocal track.In the previous research, the necessity of various methods of credit evaluation and the characteristic change of the voice according to the change of credit status were studied. In this study, we propose a personal credit discriminator by machine learning through parameters extracted through voice.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.9 no.1
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• pp.38-42
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• 1998

It has been reported that Tonsillectomy ＆ Adenoidectomy(T & A) resulted in the change of voice by structural changes directly to the vocal track. We studied the effect of T & A on the voice of patients comparing the pre-operative to the post-operative voice. It was performed using a Computerized Speech Lab(CSL50) which is currently used as a method for voice analysis. Forty-five patients who had T＆A, aging from 3 to 42 years old, took part in studies and wert evaluated for voice changes and the degree of formant changes of four basic vowels, /a/, /i/, /o/, and /u/. They were evaluated pre-operatively and post-operatively one month later using MDVP, CSL program of CSL50. The results obtained were as follows ; In using MDVP, there were some differences between pre-operative and post-operative shimmer measures within the normal range but other acoustic measures(Fo, jitter, NHR) show no significant differences(p>0.05). F3 of /a/ and /o/ were significantly decreased(p<0.05) and F2, F3 of /i/ were increased(p>0.05) in patients who only had Tonsillectomy in doing CSL spectrogram. For the patients who had T & A, Fl and F3 of /a/, F3 of /i/, Fl, F2 and F3 of /o/ were decreased with significant increase in F1 and F2 of /i/(p<0.05).