• 음성과학
• /
• 제9권2호
• /
• pp.135-146
• /
• 2002

Generally a speaker verification system improves its system recognition ratio by regularizing log likelihood ratio, using a speaker model and its background speaker model that are required to be verified. The speaker-based cohort method is one of the methods that are widely used for selecting background speaker model. Recently, Gaussian-based cohort model has been suggested as a virtually synthesized cohort model, and unlike a speaker-based model, this is the method that chooses only the probability distributions close to basic speaker's probability distribution among the several neighboring speakers' probability distributions and thereby synthesizes a new virtual speaker model. It shows more excellent results than the existing speaker-based method. This study compared the existing speaker-based background speaker models and virtual speaker models and then constructed new virtual background speaker model groups which combined them in a certain ratio. For this, this study constructed a speaker verification system that uses GMM (Gaussin Mixture Model), and found that the suggested method of selecting virtual background speaker model shows more improved performance.

• 한국항행학회논문지
• /
• 제27권6호
• /
• pp.888-896
• /
• 2023

항공기 착륙 시에는 정해진 절차에 따라 접근이 이루어진 다음, 활주로 중심선과 정렬하여 착륙하게 된다. 하지만 공항의 상황, 주변 항공기의 상황, 또는 관제사의 지시 등에 따라 빈번한 레이더 벡터링이 일어나기 때문에, 교통 흐름을 파악하거나, 비행 안전성을 파악하기 위해서는 항공기의 접근 패턴을 인지할 필요가 있다. 또한 최종 접근 시 활주로 중심선과 정렬하는 과정에서 과이탈이 발생하는 경우가 있는 데, 이는 이후 불안정 접근 등과 같이 보다 위험한 상황을 초래할 수 있다. 본 논문에서는 클러스터링 기법을 이용하여 접근 구간에서의 항공기 궤적들의 패턴을 추출하였다. GMM (Gaussian Mixture Model)을 이용하여 김해공항 접근 항공기 궤적에 대한 클러스터링을 진행하였으며, 2019년 1년간 김해공항으로 착륙한 항공기의 데이터를 이용하였다. 클러스터 별 centroid 값을 이용하여, 총 86개의 접근 궤적 패턴을 추출하였다. 그 후 각 클러스터 내 항공기 중 최종 접근시 과이탈하는 항공기를 탐지하여 확률 분포를 계산하였다.

• 음성과학
• /
• 제12권3호
• /
• pp.105-114
• /
• 2005

It was verified that a speaker verification system improved its performances of EER by regularizing log likelihood ratio, using background speaker models. Recently the wireless mobile phones are becoming more dominant communication terminals than wired phones. So the need for building a speaker verification system on mobile phone is increasing abruptly. Therefore in this paper, we had some experiments to examine the performance of speaker verification based on mobile phone's voices. Especially we are focused on the performance variations in EER(Equal Error Rate) according to several background speaker's characteristics, such as selecting methods(MSC, MIX), number of background speakers, aging factor of speech database. For this, we constructed a speaker verification system that uses GMM(Gaussin Mixture Model) and found that the MIX method is generally superior to another method by about 1.0% EER. In aspect of number of background speakers, EER is decreasing in proportion to the background speakers populations. As the number is increasing as 6, 10 and 16, the EERs are recorded as 13.0%, 12.2%, and 11.6%. An unexpected results are happened in aging effects of the speech database on the performance. EERs are measured as 4%, 12% and 19% for each seasonally recorded databases from session 1 to session 3, respectively, where duration gap between sessions is set by 3 months. Although seasons speech database has 10 speakers and 10 sentences per each, which gives less statistical confidence to results, we confirmed that enrolled speaker models in speaker verification system should be regularly updated using the ongoing claimant's utterances.