• Journal of Convergence for Information Technology
• /
• v.10 no.8
• /
• pp.35-39
• /
• 2020

DNN error is small compared to the conventional speech recognition system, DNN is difficult to parallel training, often the amount of calculations, and requires a large amount of data obtained. In this paper, we generate a phoneme unit to estimate the GMM parameters with each phoneme model parameters from the GMM to solve the problem efficiently. And it suggests ways to improve performance through clustering for a specific vocabulary to effectively apply them. To this end, using three types of word speech database was to have a DB build vocabulary model, the noise processing to extract feature with Warner filters were used in the speech recognition experiments. Results using the proposed method showed a 97.9% recognition rate in speech recognition. In this paper, additional studies are needed to improve the problems of improved over fitting.

• The Journal of the Acoustical Society of Korea
• /
• v.13 no.1
• /
• pp.24-31
• /
• 1994

In this paper, we report on the development of a speaker independent continuous speech recognition system using continuous hidden Markov models. The continuous hidden Markov model consists of mean and covariance matrices and directly models speech signal parameters, therefore does not have quantization error. Filter bank coefficients with their 1st and 2nd-order derivatives are used as feature vectors to represent the dynamic features of speech signal. We use the segmental K-means algorithm as a training algorithm and triphone as a recognition unit to alleviate performance degradation due to coarticulation problems critical in continuous speech recognition. Also, we use the one-pass search algorithm that Is advantageous in speeding-up the recognition time. Experimental results show that the system attains the recognition accuracy of $83\%$ without grammar and $94\%$ with finite state networks in speaker-indepdent speech recognition.

• Annual Conference on Human and Language Technology
• /
• 2012.10a
• /
• pp.215-218
• /
• 2012

본 논문에서는 한국어 대화 처리를 위한 통계기반 음성언어이해 시스템에 대해 기술한다. 음성언어이해시스템은 대화처리에서 음성 인식된 문장으로부터 사용자의 의도를 인식하여 의미표현으로 표현하는 기능을 담당한다. 한국어의 특성을 반영한 실용적인 음성언어이해 시스템을 위해서 강건성과 적용성, 확장성 등이 요구된다. 이를 위해 본 시스템은 음성언어의 특성상 구조분석을 하지 않고, 마이닝 기법을 이용하여 사용자 의도 표현을 생성하는 방식을 취하고 있다. 또한 한국어에서 나타나는 특징들에 대한 처리를 위해 자질 추가 및 점규화 처리 등을 수행하였다. 정보서비스용 대화처리 시스템을 대상으로 개발되고 있고, 차량 정보서비스용 학습 코퍼스를 대상으로 실험을 하여 문장단위 정확률로 약 89%의 성능을 보이고 있다.

• The Journal of the Acoustical Society of Korea
• /
• v.20 no.7
• /
• pp.55-62
• /
• 2001

In this paper, we propose a new adaptative pruning algorithm which effectively reduces the search space during the recognition process. As maximum probabilities between neighbor frames are highly interrelated, an efficient pruning threshold value can be obtained from the maximum probabilities of previous frames. The main idea is to update threshold at the present frame by a combination of previous maximum probability and hypotheses probabilities. As present threshold is obtained in on-going recognition process, the algorithm does not need any pre-experiments to find threshold values even when recognition tasks are changed. In addition, the adaptively selected threshold allows an improvement of recognition speed under different environments. The proposed algorithm has been applied to a Korean Address recognition system. Experimental results show that the proposed algorithm reduces the search space of average 14.4% and 9.14% respectively while preserving the recognition accuracy, compared to the previous method of using fixed pruning threshold values and variable pruning threshold values.

• Proceedings of the Acoustical Society of Korea Conference
• /
• 1998.06c
• /
• pp.99-102
• /
• 1998

본 연구에서는 연속음 인식과 합성을 위한 경계강도 예측 모델을 제안한다. 운율 경계 강도는 음성 합성에서는 운율구 사이의 휴지기의 길이 조절로 합성음의 자연도에 기여를 하고 연속음 인식에서는 인식과정에서 나타나는 후보문장의 선별 과정에 특징변수가 되어 인식률 향상에 큰 역할을 한다. 음성학적으로 발화된 문장은 큰 경계 단위로 볼 때 운율구 형태로 이루어졌다고 볼 수 있으며 구의 경계는 문장의 문법적인 특징과 관련을 지을 수 있게 된다. 본 논문에서는 운율 경계 강도 수준을 4로 하고 문법적인 특징으로는 트리구조 방법으로 결정된 오른쪽 가지의 수식의 깊이(rd)와 link grammar방법으로 결정된 음절수(syl), 연결거리(torig)를 bigram 모형과 결합하여 운율적 경계 강도를 예측한다. 예측 모형으로는 다중 회귀 모형과 Marcov 모형을 제안한다. 이들 모형으로 낭독체 200 문장에 대해 실험한 결과 76%로 경계 강도를 예측할 수 있었다.

• Journal of Korea Game Society
• /
• v.8 no.4
• /
• pp.107-114
• /
• 2008

The requests for speech-recognition for multi-language in field of game and the necessity of multi-language system, which expresses one phonetic model from many different kind of language phonetics, has been increased in field of game industry. Here upon, the research regarding development of multi-national language system which can express speeches, that is consist of various different languages, into only one lexical model is needed. In this paper is basic research for establishing integrated system from multi-language lexical model, and it shows the system which recognize Korean and English speeches into IPA(International Phonetic Alphabet). We focused on finding the IPA model which is satisfied with Korean and English phoneme one simutaneously. As a result, we could get the 90.62% of Korean speech-recognition rate, also 91.71% of English speech-recognition rate.

• Journal of Internet Computing and Services
• /
• v.24 no.2
• /
• pp.11-18
• /
• 2023

Recently, in the field of Speech Emotion Recognition (SER), many studies have been conducted to improve accuracy using voice features and modeling. In addition to modeling studies to improve the accuracy of existing voice emotion recognition, various studies using voice features are being conducted. This paper, voice files are separated by time interval in a time series method, focusing on the fact that voice emotions are related to time flow. After voice file separation, we propose a model for classifying emotions of speech data by extracting speech features Mel, Chroma, zero-crossing rate (ZCR), root mean square (RMS), and mel-frequency cepstrum coefficients (MFCC) and applying them to a recurrent neural network model used for sequential data processing. As proposed method, voice features were extracted from all files using 'librosa' library and applied to neural network models. The experimental method compared and analyzed the performance of models of recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) using the Interactive emotional dyadic motion capture Interactive Emotional Dyadic Motion Capture (IEMOCAP) english dataset.

• The Journal of the Acoustical Society of Korea
• /
• v.22 no.7
• /
• pp.528-538
• /
• 2003

A SCCRS (Speech and Character Combined Recognition System) is developed for working on mobile devices such as PDA (Personal Digital Assistants). In SCCRS, the feature extraction is separately carried out for speech and for hand-written character, but the recognition is performed in a common engine. The recognition engine employs essentially CHMM (Continuous Hidden Markov Model), which consists of variable parameter topology in order to minimize the number of model parameters and to reduce recognition time. For generating contort independent variable parameter model, we propose the SSMS(Successive State and Mixture Splitting), which gives appropriate numbers of mixture and of states through splitting in mixture domain and in time domain. The recognition results show that the proposed SSMS method can reduce the total number of GOPDD (Gaussian Output Probability Density Distribution) up to 40.0% compared to the conventional method with fixed parameter model, at the same recognition performance in speech recognition system.

• The Journal of the Acoustical Society of Korea
• /
• v.18 no.2
• /
• pp.3-10
• /
• 1999

This paper describes the development and evaluation of a Korean address input system employing automatic speech recognition technique as user interface for input Korean address. Address consists of cities, provinces and counties. The system works on a window 95 environment of personal computer with built-in soundcard. In the speech recognition part, the Continuous density Hidden Markov Model(CHMM) for making phoneme like units(PLUs) and One Pass Dynamic Programming(OPDP) algorithm is used for recognition. For address recognition, Finite State Automata(FSA) suitable for Korean address structure is constructed. To achieve an acceptable performance against the variation of speakers, microphones, and environmental noises, Maximum a posteriori(MAP) estimation is implemented in adaptation. And to improve the recognition speed, fast search method using variable pruning threshold is newly proposed. In the evaluation tests conducted for the 100 connected words uttered by 3 males the system showed above average 96.0% of recognition accuracy for connected words after adaption and recognition speed within 2 seconds, showing the effectiveness of the system.

• The Journal of the Acoustical Society of Korea
• /
• v.17 no.4
• /
• pp.3-13
• /
• 1998

본 논문은 음성 다이어링 시스템을 구현하기 위한 한국어 단독 숫자음 및 연속 숫 자음 인식에 관한 것이다. 단독 숫자음의 인식은 미지의 입력 음성을 재귀 신경망을 이용하 여 모델링된 각 모델에 인가하고, 신경 회로망의 출력 노드의 상태열을 검사하여 적절한 상 태 전이를 하며 최고의 확률값을 출력하는 모델을 인식된 결과로 출력한다. 연속 숫자음의 인식은 미지의 연속 숫자음을 재귀 신경 회로망을 이용한 연속 숫자음 모델에 입력하고, 신 경 회로망의 출력에 대하여 적절한 상태 전이에 대한 검사와 레벨 빌딩(Level Building)을 수행하여 최소의 오차를 가지는 모델열을 인식된 결과로 출력한다. 재귀 신경 회로망을 이 용하여 음절 모델을 만드는 과정에서 재귀 노드는 예상치가 주어지지 않으므로 신경 회로망 의 학습에서 제외되어 현저한 학습 속도의 저하를 가져온다. 따라서 본 논문에서는 재귀 신 경 회로망의 학습 속도를 향상시키기 위한 2가지 방법을 제안 한다. 첫 번째는 재귀 신경 회로망의 재귀 노드의 예상치를 실험적으로 주어줌으로써 학습 속도의 향상을 도모하였다. 두 번째는 음절 모델의 출력노드의 개수와 음절 모델의 세그먼트 경계를 알고리듬을 이용하 여 자동적으로 조절하였다. 실험결과, 단독어의 경우 음절 '에'에 포함하는 한국어 11개의 숫 자음에 대하여 화자 종속의 경우 97.3%, 화자 독립의 경우 80.5%의 인식률을 얻었으며, 연 속 숫자음의 경우는 21종류의 연속 숫자음에 대하여 화자 종속에서 88.2%, 화자 독립의 경 우 81.3%의 인식률을 얻을 수 있었다.