• 대한음성학회:학술대회논문집
• /
• 대한음성학회 2003년도 10월 학술대회지
• /
• pp.185-188
• /
• 2003

In this paper, we construct phonetic GMM for text-independent speaker identification system. The basic idea is to combine of the advantages of baseline GMM and HMM. GMM is more proper for text-independent speaker identification system. In text-dependent system, HMM do work better. Phonetic GMM represents more sophistgate text-dependent speaker model based on text-independent speaker model. In speaker identification system, phonetic GMM using HMM-based speaker-independent phoneme recognition results in better performance than baseline GMM. In addition to the method, N-best recognition algorithm used to decrease the computation complexity and to be applicable to new speakers.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제51권2호
• /
• pp.69-77
• /
• 2002

Speaker verification can be classified in two categories, text-dependent speaker verification and text-independent speaker verification. In this paper, we discuss text-dependent speaker verification. Text-dependent speaker verification system determines whether the sound characteristics of the speaker are equal to those of the specific person or not. In this paper we obtain the speaker data using a sound card in various noisy conditions, apply a new Hidden LMS (Least Mean Square) adaptive algorithm to it, and extract LPC (Linear Predictive Coding)-cepstrum coefficients as feature vectors. Finally, we use a competitive learning neural network for speaker verification. The proposed hidden LMS adaptive filter using a neural network reduces noise and enhances features in various noisy conditions. We construct a separate neural network for each speaker, which makes it unnecessary to train the whole network for a new added speaker and makes the system expansion easy. We experimentally prove that the proposed method improves the speaker verification performance.

• 한국정보통신학회논문지
• /
• 제10권4호
• /
• pp.739-743
• /
• 2006

본 논문은 신경회로망을 이용한 화자적응 음성인식 제어시스템을 개발하였다. 화자종속시스템은 단일 화자의 음성만 등록하여 이용하므로 여러 화자의 음성을 인식하는 데는 문제가 있고, 화자독립시스템은 여러 화자를 인식한다. 본 연구 개발에서는 화자적응시스템을 구현하여 화자종속형의 단점을 보완하여 화자 독립과 화자 종속을 혼합하여 사용 할 수 있는 기능으로 화자 적용방법으로 구현하였고, 화자인증(Speaker Verification)도 가능하도록 프로그램 하였다.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 1999년도 추계종합학술대회 논문집
• /
• pp.663-667
• /
• 1999

In this paper, we review the conventional speaker verification algorithm and present the text-dependent speaker verification system for application over telephone lines and its result of experiments. We apply blind-segmentation algorithm which segments speech into sub-word unit without linguistic information to the speaker verification system for training speaker model effectively with limited enrollment data. And the World-mode] that is created from PBW DB for score normalization is used. The experiments are presented in implemented system using database, which were constructed to simulate field test, and are shown 3.3% EER.

• 한국음향학회지
• /
• 제19권6호
• /
• pp.34-40
• /
• 2000

문장 종속형 화자 확인시스템을 구현하는데 있어 화자가 발음할 문장의 선정은 화자인식시스템의 성능을 좌우하는 중요한 사항이다. 본 연구에서는 한국어의 음가 분류방식을 이용하여 자음조합체계를 구축하고 이를 웹 기반 화자확인시스템에 적용하여 급격한 화자음성정보의 변화에 대응하는 동시에 최적의 인식성능을 낼 수 있는 자음조합방식을 도출하였다.

• 대한음성학회지:말소리
• /
• 제61호
• /
• pp.113-123
• /
• 2007

The environmental differences between training and testing mode are generally considered to be the critical factor for the performance degradation in speaker recognition systems. Especially, general speaker recognition systems try to get as clean speech as possible to train the speaker model, but it's not true in real testing phase due to environmental and channel noise. So in this paper, the new method of weighting the frame-based likelihood according to frame SNR is proposed in order to cope with that problem. That is to make use of the deep correlation between speech SNR and speaker discrimination rate. To verify the usefulness of this proposed method, it is applied to the context dependent speaker identification system. And the experimental results with the cellular phone speech DB which is designed by ETRI for Koran speaker recognition show that the proposed method is effective and increase the identification accuracy by 11% at maximum.

• 대한음성학회지:말소리
• /
• 제47호
• /
• pp.131-140
• /
• 2003

We propose a new method of generating client models for HMM based text-dependent speaker verification system with only a small amount of training data. To make a client model, statistical methods such as segmental K-means algorithm are widely used, but they do not guarantee the quality or reliability of a model when only limited data are avaliable. In this paper, we propose a blind speech segmentation based on level building DTW algorithm as an alternative method to make a client model with limited data. In addition, considering the fact that voiced sounds have much more speaker-specific information than unvoiced sounds and energy of the former is higher than that of the latter, we also propose a new score evaluation method using the observation probability raised to the power of weighting factor estimated from the normalized log energy. Our experiment shows that the proposed methods are superior to conventional HMM based speaker verification system.

• 한국음향학회지
• /
• 제19권4호
• /
• pp.23-30
• /
• 2000

본 연구에서는 인터넷 웹 기반의 화자확인시스템을 설계하였다. 웹 기반의 화자확인 시스템에 적용할 화자인식기법을 선정하기 위해 문자종속 화자인식기법들(DTW, DHMM, SCHMM)의 성능 및 특징들을 컴퓨터 시뮬레이션을 통하여 비교 평가하였다. 컴퓨터 시뮬레이션 결과를 이용하여 웹 기반의 화자확인시스템에 적합한 인식성능 및 초기 학습발음수를 갖는 DHMM을 화자인식기법으로 선정하고 이를 분산처리환경에서 동작하도록 Activex, DCOM기술을 이용하여 3계층방식으로 설계하였다.

• 대한음성학회지:말소리
• /
• 제53호
• /
• pp.143-156
• /
• 2005

Speaker adaptation in eigenvoice space is a popular method for rapid speaker adaptation. To improve the performance of the method, the number of speaker dependent models should be increased and eigenvoices should be re-estimated. However, principal component analysis takes much time to find eigenvoices, especially in a continuous speech recognition system. This paper describes a method to reduce computation time to estimate eigenvoices only for supplementary speaker dependent models and to merge them with the used eigenvoices. Experiment results show that the computation time is reduced by 73.7% while the performance is almost the same in case that the number of speaker dependent models is the same as used ones.

• ETRI Journal
• /
• 제45권4호
• /
• pp.678-689
• /
• 2023

This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency (F₀), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.