• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.32 no.2
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• pp.87-93
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• 2021

There are a number of voice analysis programs around the world. Domestic voice analysis is performed by relying heavily on specific commercial program. We intend to develop coding for voice analysis using Praat and apply it to clinical practice. This study consisted of Experiment 1 and Experiment 2. Experiment 1 was the development of automated voice analysis coding based on Praat. The coding was largely divided into a recording, an analysis, and a storage section. Experiment 2 was applied to the voice analysis of 2 male patients pre- and post-operation with this coding. The analysis parameters of this coding provided 26 parameters for vowel /a/, nine parameters for sentence analysis, and a total of 4 parameters for voice range profile analysis. In two male patients, the pitch and the intensity increased, the voice quality improved, and the sentence length decreased after surgery. The coding was well made, so the output was good in real time. The code is automated as much as possible to block manual errors and increases convenience and efficiency by generating the result sheet in real time.

• Korean Journal of Bronchoesophagology
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• v.7 no.2
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• pp.161-167
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• 2001

Background and Objectives： Total laryngectomy is often required for advanced cases. But this operation induced the many inconvenience of basic daily life. Early diagnosis of laryngeal cancer is very important to prevent from this disastrous condition. In this point of view, mass screening test for early detection of laryngeal cancer is necessary. Screening test using voice has many advantages such as simple, less interventional. Voice collection by Automatic Response System(ARS) is comfortable and easy to got acoustic sample. Thus author tried to got the acoustic parameters which can differentiate normal, benign. and malignant laryngeal diseases and also checked the availability of parameters on neural network system. Materials and Methods: Author has evaluated the voice from 17 laryngeal cancer patients and 45 benign laryngeal disease patients who visited at Department of Otolaryngology, Pusan National University Hospital from May 1998 to April 2001, and 15 normal control. Author chose the sir Parameters (Jitt. vFo, Shim, vAm, NHR, SPI) that was thought to be related with voice collected by ARS among thirty-three parameters analysed by a Multi-Dimensional Voice Program (MDVP). Two-step neural network was used for the availability of six parameters. Results: The detection rate of normal voice by ARS voice analysis is 78.5% and detection rate of abnormal voice was 97.1 o/o. Among abnormal voice, the detection rate of benign laryngeal diseases and laryngeal cancers were 82.4 o/o, 70.6% respectively. Conclusion: Author concluded that six parameters and Matlab based neural network software may be effective in development of acoustic screening system for laryngeal cancer and further study should be necessary for development of new acoustic parameters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.7C
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• pp.538-543
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• 2012

The performance of the speech recognition engine is one of the most critical elements of the in-vehicle speech recognition interface. The objective of this paper is to develop an automated platform for running performance tests on the in-vehicle speech recognition engine. The developed platform comprise of main program, agent program, database management module, and statistical analysis module. A simulation environment for performance tests which mimics the real driving situations was constructed, and it was tested by applying pre-recorded driving noises and a speaker's voice as inputs. As a result, the validity of the results from the speech recognition tests was proved. The users will be able to perform the performance tests for the in-vehicle speech recognition engine effectively through the proposed platform.

• The Korean Journal of Air & Space Law and Policy
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• v.23 no.2
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• pp.137-172
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• 2008

The earliest safety data programs, the FDR and CVR, were electronic reporting systems that generate data "automatically." The FDR program, originally instituted in 1958, had no publicly available restrictions for protections against sanctions by the FAA or an airline, although there are agreements and union contracts forbidding the use of FDR data for FAA enforcement actions. This FDR program still has the least formalized protections. With the advent of the CVR program in 1966, the precursor to the current FAR 91.25 was already in place, having been promulgated in 1964. It stated that the FAA would not use CVR data for enforcement actions. In 1982, Congress began restricting the disclosure of the CVR tape and transcripts. Congress added further clarification of the availability of discovery in civil litigation in 1994. Thus, the CVR data have more definitive protections in place than do FDR data. The ASRS was the first non-automatic reporting system; and built into its original design in 1975 was a promise of limited protection from enforcement sanctions. That promise was further codified in an FAR in 1979. As with the CVR, from its inception, the ASRS had some protections built in for the person who might have had a safety problem. However, the program did not (and to this day does not) explicitly deal with issues of use by airlines, litigants, or the public media, although it appears that airlines will either take a non-punitive stance if an ASRS report is filed, or the airline may ignore the fact that it has been filed at all. The FAA worked with several U.S. airlines in the early 1990s on developing ASAP programs, and the FAA issued an Advisory Circular about the program in 1997. From its inception, the ASAP program contained some FAA enforcement protections and company discipline protections, although some protection against litigation disclosure and public disclosure was not added until 2003, when FAA Order 8000.82 was promulgated, placing the program under the protections of FAR 193, which had been added in 2001. The FOQA program, when it was first instituted through a demonstration program in 1995, did not contain protections against sanctions. Now, however, the FAA cannot take enforcement action based on FOQA safety data, and an airline is limited to "corrective action" under the program. Union contracts can exclude FOQA from the realm of disciplinary action, although airline practice may be for airlines to require retraining if there is no contract in place forbidding it. The data is protected against disclosure for litigation and public media purposes by FAA Order 8000.81, issued in 2003, which placed FOQA under the protections of FAR 193. The figure on the next page shows when each program began, and when each statute, regulation, or order became effective for that program.