• Title, Summary, Keyword: HMM

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Control of Duration Model Parameters in HMM-based Korean Speech Synthesis (HMM 기반의 한국어 음성합성에서 지속시간 모델 파라미터 제어)

  • Kim, Il-Hwan;Bae, Keun-Sung
    • Speech Sciences
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    • v.15 no.4
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    • pp.97-105
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    • 2008
  • Nowadays an HMM-based text-to-speech system (HTS) has been very widely studied because it needs less memory and low computation complexity and is suitable for embedded systems in comparison with a corpus-based unit concatenation text-to-speech one. It also has the advantage that voice characteristics and the speaking rate of the synthetic speech can be converted easily by modifying HMM parameters appropriately. We implemented an HMM-based Korean text-to-speech system using a small size Korean speech DB and proposes a method to increase the naturalness of the synthetic speech by controlling duration model parameters in the HMM-based Korean text-to speech system. We performed a paired comparison test to verify that theses techniques are effective. The test result with the preference scores of 73.8% has shown the improvement of the naturalness of the synthetic speech through controlling the duration model parameters.

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Echo Noise Robust HMM Learning Model using Average Estimator LMS Algorithm (평균 예측 LMS 알고리즘을 이용한 반향 잡음에 강인한 HMM 학습 모델)

  • Ahn, Chan-Shik;Oh, Sang-Yeob
    • Journal of Digital Convergence
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    • v.10 no.10
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    • pp.277-282
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    • 2012
  • The speech recognition system can not quickly adapt to varied environmental noise factors that degrade the performance of recognition. In this paper, the echo noise robust HMM learning model using average estimator LMS algorithm is proposed. To be able to adapt to the changing echo noise HMM learning model consists of the recognition performance is evaluated. As a results, SNR of speech obtained by removing Changing environment noise is improved as average 3.1dB, recognition rate improved as 3.9%.

Telephone Digit Speech Recognition using Discriminant Learning (Discriminant 학습을 이용한 전화 숫자음 인식)

  • 한문성;최완수;권현직
    • Journal of the Institute of Electronics Engineers of Korea TE
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    • v.37 no.3
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    • pp.16-20
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    • 2000
  • Most of speech recognition systems are using Hidden Markov Model based on statistical modelling frequently. In Korean isolated telephone digit speech recognition, high recognition rate is gained by using HMM if many training data are given. But in Korean continuous telephone digit speech recognition, HMM has some limitations for similar telephone digits. In this paper we suggest a way to overcome some limitations of HMM by using discriminant learning based on minimal classification error criterion in Korean continuous telephone digit speech recognition. The experimental results show our method has high recognition rate for similar telephone digits.

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Effect of Fixation Methods on the Flame Retardant and Performance Properties of MDPPA/HMM treated Cotton (MDPPA/HMM처리 면직물의 고착방법에 따른 방염성과 물성의 변화)

  • 지주원;오경화
    • Journal of the Korean Society of Clothing and Textiles
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    • v.24 no.1
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    • pp.15-23
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    • 2000
  • Effect of fixation methods on the flame retardant(FR) and performance properties of MDPPA/HMM treated cotton fabrics were studied. Combination of three different fixation methods - premercerization, swelling agent treatment, pad dry cure fixation, and wet fixation - were applied to flame retardant finish of cotton with MDPPA/HMM. As a result, an increase in internal volume of cotton fiber by pre-mercerization and addition of swelling agent, and wet fixation increased %add-on of FR agent improving FR efficiency and wash fastness. Tensile strength of MDPPA/HMM treated cotton fabrics by wet fixation and swelling agent were slightly decreased, but that of premercerized cotton was improved. Wet fixated fabric showed lower bending rigidity and better compressional properties which improved fabric hand. Retention of swelling ability of cotton treated with MDPPA/HMM improved moisture absorption properties.

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Discrete HMM Training Algorithm for Incomplete Time Series Data (불완전 시계열 데이터를 위한 이산 HMM 학습 알고리듬)

  • Sin, Bong-Kee
    • Journal of Korea Multimedia Society
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    • v.19 no.1
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    • pp.22-29
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    • 2016
  • Hidden Markov Model is one of the most successful and popular tools for modeling real world sequential data. Real world signals come in a variety of shapes and variabilities, among which temporal and spectral ones are the prime targets that the HMM aims at. A new problem that is gaining increasing attention is characterizing missing observations in incomplete data sequences. They are incomplete in that there are holes or omitted measurements. The standard HMM algorithms have been developed for complete data with a measurements at each regular point in time. This paper presents a modified algorithm for a discrete HMM that allows substantial amount of omissions in the input sequence. Basically it is a variant of Baum-Welch which explicitly considers the case of isolated or a number of omissions in succession. The algorithm has been tested on online handwriting samples expressed in direction codes. An extensive set of experiments show that the HMM so modeled are highly flexible showing a consistent and robust performance regardless of the amount of omissions.

An EMG Signals Discrimination Using Hybrid HMM and MLP Classifier for Prosthetic Arm Control Purpose (의수 제어를 위한 HMM-MLP 근전도 신호 인식 기법)

  • 권장우;홍승홍
    • Journal of Biomedical Engineering Research
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    • v.17 no.3
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    • pp.379-386
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    • 1996
  • This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) and hidden Markov models (HMM's) hybrid classifier. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or vari- ous time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrasts the multilayer feedforward networks are suitable for static patterns. Ank a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of MLP and HMM algorithms that might lead to further improved EMG recognition systems.

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A Study of Phoneme Modeling for Improvement of Automatic Segmentation Performance (자동 음소 분할 성능 개선을 위한 음소 모델링에 관한 연구)

  • Park Hae Young;Kim Hyung Soon
    • Proceedings of the Acoustical Society of Korea Conference
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    • pp.175-178
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    • 2002
  • 본 논문에서는 Hidden Markov Model(HMM)을 이용하여 corpus 기반 TTS에 사용할 DB를 자동 음소 분할 해주는 시스템을 구현하였다. HMM을 이용해서 음소 분할 할 경우 HMM을 모델링 하는 방법에 따라 많은 성능의 차이가 난다. 따라서 본 논문에서는 HMM 모델링 방법에 따른 몇 가지 실험 및 성능 평가를 하였다. 실험 결과 음성 인식과는 달리 HMM모델링 시 triphone 모델보다 monophone 모델의 성능이 더 우수하였으며, 에너지 기반의 후처리를 통해 성능 향상을 얻을 수 있었다.

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Segmental Corrective Training for HMM Parameter Estimation in Speech Recognition (음성인식 시스템의 HMM 파라메터 추정을 위한 분절단위 교정 학습)

  • 김회린;이황수
    • The Journal of the Acoustical Society of Korea
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    • v.12 no.2E
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    • pp.5-11
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    • 1993
  • 본 논문에서 HMM 파라메터 추정을 위해 분절단위 정보를 이용하는 수정된 교정학습방법을 제안한다. 수정된 교정학습방법은 기존의 교정학습 방법에서 사용하는 전향·후향 알고리즘 대신에 분절단위 K-means 알고리즘을 사용하여 HMM 파라메터를 교정한다. 이 방식은 분절단위 K-means 알고리즘이 음성신호내의 공통의 통계적 특성을 가지는 상태단위 정보를 강조한다는 사실을 이용하였다. 화자종속 음소 및 단어인식 실험에서 제안된 알고리즘이 기존의 교정학습 방법보다 적은 계산량으로도 향상된 인식률을 보여주었다. 이것은 HMM 교정학습에서 상태다누이 정보가 중요함을 보여준다.

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A study on recognition improvement of velopharyngeal insufficiency patient's speech using various types of deep neural network (심층신경망 구조에 따른 구개인두부전증 환자 음성 인식 향상 연구)

  • Kim, Min-seok;Jung, Jae-hee;Jung, Bo-kyung;Yoon, Ki-mu;Bae, Ara;Kim, Wooil
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.6
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    • pp.703-709
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    • 2019
  • This paper proposes speech recognition systems employing Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) structures combined with Hidden Markov Moldel (HMM) to effectively recognize the speech of VeloPharyngeal Insufficiency (VPI) patients, and compares the recognition performance of the systems to the Gaussian Mixture Model (GMM-HMM) and fully-connected Deep Neural Network (DNNHMM) based speech recognition systems. In this paper, the initial model is trained using normal speakers' speech and simulated VPI speech is used for generating a prior model for speaker adaptation. For VPI speaker adaptation, selected layers are trained in the CNN-HMM based model, and dropout regulatory technique is applied in the LSTM-HMM based model, showing 3.68 % improvement in recognition accuracy. The experimental results demonstrate that the proposed LSTM-HMM-based speech recognition system is effective for VPI speech with small-sized speech data, compared to conventional GMM-HMM and fully-connected DNN-HMM system.

Two-Dimensional Model of Hidden Markov Lattice (이차원 은닉 마르코프 격자 모형)

  • 신봉기
    • Journal of Korea Multimedia Society
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    • v.3 no.6
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    • pp.566-574
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    • 2000
  • Although a numbed of variants of 2D HMM have been proposed in the literature, they are, in a word, too simple to model the variabilities of images for diverse classes of objects; they do not realize the modeling capability of the 1D HMM in 2D. Thus the author thinks they are poor substitutes for the HMM in 2D. The new model proposed in this paper is a hidden Markov lattice or, we can dare say, a 2D HMM with the causality of top-down and left-right direction. Then with the addition of a lattice constraint, the two algorithms for the evaluation of a model and the maximum likelihood estimation of model parameters are developed in the theoretical perspective. It is a more natural extension of the 1D HMM. The proposed method will provide a useful way of modeling highly variable patterns such as offline cursive characters.

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