• Title/Summary/Keyword: Speaker segmentation

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Speaker Segmentation System Using Eigenvoice-based Speaker Weight Distance Method (Eigenvoice 기반 화자가중치 거리측정 방식을 이용한 화자 분할 시스템)

  • Choi, Mu-Yeol;Kim, Hyung-Soon
    • The Journal of the Acoustical Society of Korea
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    • v.31 no.4
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    • pp.266-272
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    • 2012
  • Speaker segmentation is a process of automatically detecting the speaker boundary points in the audio data. Speaker segmentation methods are divided into two categories depending on whether they use a prior knowledge or not: One is the model-based segmentation and the other is the metric-based segmentation. In this paper, we introduce the eigenvoice-based speaker weight distance method and compare it with the representative metric-based methods. Also, we employ and compare the Euclidean and cosine similarity functions to calculate the distance between speaker weight vectors. And we verify that the speaker weight distance method is computationally very efficient compared with the method directly using the distance between the speaker adapted models constructed by the eigenvoice technique.

Performance improvement of text-dependent speaker verification system using blind speech segmentation and energy weight (Blind speech segmentation과 에너지 가중치를 이용한 문장 종속형 화자인식기의 성능 향상)

  • Kim Jung-Gon;Kim Hyung Soon
    • MALSORI
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    • no.47
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    • pp.131-140
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    • 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.

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Speaker Identification Based on Vowel Classification and Vector Quantization (모음 인식과 벡터 양자화를 이용한 화자 인식)

  • Lim, Chang-Heon;Lee, Hwang-Soo;Un, Chong-Kwan
    • The Journal of the Acoustical Society of Korea
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    • v.8 no.4
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    • pp.65-73
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    • 1989
  • In this paper, we propose a text-independent speaker identification algorithm based on VQ(vector quantization) and vowel classification, and its performance is studied and compared with that of a conventional speaker identification algorithm using VQ. The proposed speaker identification algorithm is composed of three processes: vowel segmentation, vowel recognition and average distortion calculation. The vowel segmentation is performed automatlcally using RMS energy, BTR(Back-to-Total cavity volume Ratio)and SFBR(Signed Front-to-Back maximum area Ratio) extracted from input speech signal. If the Input speech signal Is noisy, particularity when the SNR is around 20dB, the proposed speaker identification algorithm performs better than the reference speaker identification algorithm when the correct vowel segmentation is done. The same result is obtained when we use the noisy telephone speech signal as an input, too.

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A Blind Segmentation Algorithm for Speaker Verification System (화자확인 시스템을 위한 분절 알고리즘)

  • 김지운;김유진;민홍기;정재호
    • The Journal of the Acoustical Society of Korea
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    • v.19 no.3
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    • pp.45-50
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    • 2000
  • This paper proposes a delta energy method based on Parameter Filtering(PF), which is a speech segmentation algorithm for text dependent speaker verification system over telephone line. Our parametric filter bank adopts a variable bandwidth along with a fixed center frequency. Comparing with other methods, the proposed method turns out very robust to channel noise and background noise. Using this method, we segment an utterance into consecutive subword units, and make models using each subword nit. In terms of EER, the speaker verification system based on whole word model represents 6.1%, whereas the speaker verification system based on subword model represents 4.0%, improving about 2% in EER.

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Text-dependent Speaker Verification System Over Telephone Lines (전화망을 위한 어구 종속 화자 확인 시스템)

  • 김유진;정재호
    • Proceedings of the IEEK Conference
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    • 1999.11a
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    • pp.663-667
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    • 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.

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Speaker Change Detection Based on a Graph-Partitioning Criterion

  • Seo, Jin-Soo
    • The Journal of the Acoustical Society of Korea
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    • v.30 no.2
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    • pp.80-85
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    • 2011
  • Speaker change detection involves the identification of time indices of an audio stream, where the identity of the speaker changes. In this paper, we propose novel measures for the speaker change detection based on a graph-partitioning criterion over the pairwise distance matrix of feature-vector stream. Experiments on both synthetic and real-world data were performed and showed that the proposed approach yield promising results compared with the conventional statistical measures.

Segmentation and Tracking Algorithm for Moving Speaker in the Video Conference Image (화상회의 영상에서 움직이는 화자의 분할 및 추적 알고리즘)

  • Choi Woo-Young;Kim Han-Me
    • Journal of IKEEE
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    • v.6 no.1 s.10
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    • pp.54-64
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    • 2002
  • In this paper, we propose the algorithm for segmenting the moving speaker and tracking its movement in the video conference image. For real time processing, we simplify the algorithm which is processed in the order of the segmenting and the tracking step. In the segmenting step, the speaker object is segmented from the image by using both the motion information obtained from the difference method and the illuminance information of image. The reference mask image is created from segmented speaker object. In the tracking step, the moving speaker is tracked by using simple block matching algorithm of which computation time is reduced by discarding the blocks which are classified into the unuseful blocks. In the simulation, we can get the good result of segmenting and tracking the moving speaker by applying the proposed algorithm to several test images.

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A Study on Realization of Continuous Speech Recognition System of Speaker Adaptation (화자적응화 연속음성 인식 시스템의 구현에 관한 연구)

  • 김상범;김수훈;허강인;고시영
    • The Journal of the Acoustical Society of Korea
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    • v.18 no.3
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    • pp.10-16
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    • 1999
  • In this paper, we have studied Continuous Speech Recognition System of Speaker Adaptation using MAPE (Maximum A Posteriori Probability Estimation) which can adapt any small amount of adaptation speech data. Speaker adaptation is performed by the method of MAPB after Concatenation training which is making sentence unit HMM linked by syllable unit HMM and Viterbi segmentation classifies speech data to be adaptation into segmentation of syllable unit data automatically without hand labelling. For car control speech the recognition rates of adaptation of HMM was 77.18% which is approximately 6% improvement over that of unadapted HMM.(in case of O(n)DP)

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A Study on the Speaker Adaptation of a Continuous Speech Recognition using HMM (HMM을 이용한 연속 음성 인식의 화자적응화에 관한 연구)

  • Kim, Sang-Bum;Lee, Young-Jae;Koh, Si-Young;Hur, Kang-In
    • The Journal of the Acoustical Society of Korea
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    • v.15 no.4
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    • pp.5-11
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    • 1996
  • In this study, the method of speaker adaptation for uttered sentence using syllable unit hmm is proposed. Segmentation of syllable unit for sentence is performed automatically by concatenation of syllable unit hmm and viterbi segmentation. Speaker adaptation is performed using MAPE(Maximum A Posteriori Probabillity Estimation) which can adapt any small amount of adaptation speech data and add one sequentially. For newspaper editorial continuous speech, the recognition rates of adaptation of HMM was 71.8% which is approximately 37% improvement over that of unadapted HMM

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I-vector similarity based speech segmentation for interested speaker to speaker diarization system (화자 구분 시스템의 관심 화자 추출을 위한 i-vector 유사도 기반의 음성 분할 기법)

  • Bae, Ara;Yoon, Ki-mu;Jung, Jaehee;Chung, Bokyung;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
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    • v.39 no.5
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    • pp.461-467
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    • 2020
  • In noisy and multi-speaker environments, the performance of speech recognition is unavoidably lower than in a clean environment. To improve speech recognition, in this paper, the signal of the speaker of interest is extracted from the mixed speech signals with multiple speakers. The VoiceFilter model is used to effectively separate overlapped speech signals. In this work, clustering by Probabilistic Linear Discriminant Analysis (PLDA) similarity score was employed to detect the speech signal of the interested speaker, which is used as the reference speaker to VoiceFilter-based separation. Therefore, by utilizing the speaker feature extracted from the detected speech by the proposed clustering method, this paper propose a speaker diarization system using only the mixed speech without an explicit reference speaker signal. We use phone-dataset consisting of two speakers to evaluate the performance of the speaker diarization system. Source to Distortion Ratio (SDR) of the operator (Rx) speech and customer speech (Tx) are 5.22 dB and -5.22 dB respectively before separation, and the results of the proposed separation system show 11.26 dB and 8.53 dB respectively.