피치 변환 기법을 사용한 실시간 음성 변환 알고니즘

김원구 국립7개대학공동논문집간행위원회 2002 공업기술연구 Vol.2 No.-

In this paper, real-time voice change method using pitch change technique is proposed to change one's voice to the other voice. For this purpose, sampling rate change method using DFT (Discrete Fourier Transform) method and time scale modification method using SOLA(Synchronized OverLap and Add) method is combined to change pitch. In order to evaluate the performance of the proposed method, voice transformation experiments were conducted. Experimental results showed that original speech signal is changed to the other speech signal in which original speaker's identity is difficult to find.

확률적 매칭을 사용한 화자독립 음소 HMM 기반의 음성 인식

김원구 국립7개대학공동논문집간행위원회 2004 공업기술연구 Vol.4 No.-

This paper presents a method that improves the performance of the personal voice dialling system in which speaker independent phoneme HMM's are used. Since the speaker independent phoneme HMM based voice dialing system uses only the phone transcription of the input sentence, the storage space could be reduced greatly.However, the performance of the system is worse than that of the system which uses the speaker dependent models due to the phoneme recognition errors generated when the speaker independent models are used. In order to solve this problem, a new method that jointly estimates transformation vectors for the speaker adaptation and transcriptions from training utterances is presented. The biases and transcriptions are estimated iteratively from the training data of each user with maximum likelihood approach to the stochastic matching using speaker-independent phone models. Experimental result shows that the proposed method is superior to the conventional method which used transcriptions only.

가우시안 혼합 모델을 이용한 문장 및 화자 독립 감정 인식

김원구 국립7개대학공동논문집간행위원회 2005 공업기술연구 Vol.5 No.-

The pattern recognition algorithms and feature parameters for emotion recognition are studied. In this paper, KNN algorithm was used as the pattern matching technique for comparison, and also VQ and GMM were used for speaker and context independent recognition. The speech parameters used as the feature are pitch, energy, MFCC and their first and second derivatives. Experimental results showed that emotion recognizer using MFCC and their derivatives as a feature showed better performance than that using the pitch and energy parameters. For pattern recognition algorithm, GMM based emotion recognizer was superior to KNN and VQ based recognizer.

벡터 양자화를 이용한 문장 및 화자 독립 감정 인식

김원구 국립7개대학공동논문집간행위원회 2003 공업기술연구 Vol.3 No.-

In this paper, the vector quantization based emotion recognition system is proposed for speaker and context independent emotion recognition. For this purpose, a corpus of emotional speech data recorded and classified according to the emotion using the subjective evaluation were used to make statistical feature vectors such as average, standard deviation and maximum value of pitch and energy and to evaluate the performance of the conventional pattern matching algorithms. In the Experiments, pitch and energy parameters were used as a prosodic information and MFCC parameters were used as phonetic information. Experimental results showed that vector quantization based emotion recognizer using MFCC parameters showed better performance than that using the pitch and energy parameters. The vector quantization based emotion recognizer achieved recognition rates of 73.3% for the speaker and context independent classification.

감정 적응을 이용한 감정 화자 인식

김원구(Weon-Goo Kim) 대한전기학회 2017 전기학회논문지 Vol.66 No.7

Speech with various emotions degrades the performance of the speaker recognition system. In this paper, a speaker recognition method using emotional adaptation has been proposed to improve the performance of speaker recognition system using affective speech. For emotional adaptation, emotional speaker model was generated from speaker model without emotion using a small number of training affective speech and speaker adaptation method. Since it is not easy to obtain a sufficient affective speech for training from a speaker, it is very practical to use a small number of affective speeches in a real situation. The proposed method was evaluated using a Korean database containing four emotions. Experimental results show that the proposed method has better performance than conventional methods in speaker verification and speaker recognition.

감정 적응을 이용한 감정 인식 학습 방법

김원구(Weon-Goo Kim) 한국전기전자학회 2020 전기전자학회논문지 Vol.24 No.4

본 논문에서는 기존 감정 인식 시스템의 성능 향상을 위하여 감정 적응을 사용한 감정 학습 방법이 제안되었다. 감정 적응을 위하여 적은 개수의 학습 감정 음성과 감정 적응 방식을 사용하여 감정이 없는 음성 모델로부터 감정 음성 모델이 생성되었다. 이러한 방법은 기존 방법보다 적은 개수의 감정 음성을 사용하여도 우수한 성능을 나타내었다. 학습을 위하여 충분한 감정 음성을 얻는 것은 쉽지 않기 때문에 적은 개수의 감정 음성을 사용하는 것은 실제 상황에서 매우 실용적이다. 4가지 감정이 포함된 한국어 데이터베이스를 사용한 실험 결과에서 감정 적응을 이용한 제안된 방법이 기존 방법보다 우수한 성능을 나타내었다. In this paper, an emotion training method using emotional adaptation is proposed to improve the performance of the existing emotion recognition system. For emotion adaptation, an emotion speech model was created from a speech model without emotion using a small number of training emotion voices and emotion adaptation methods. This method showed superior performance even when using a smaller number of emotional voices than the existing method. Since it is not easy to obtain enough emotional voices for training, it is very practical to use a small number of emotional voices in real situations. In the experimental results using a Korean database containing four emotions, the proposed method using emotional adaptation showed better performance than the existing method.

피치 변환을 사용한 실시간 음성 변환 시스템

김원구(Weon-Goo Kim) 한국지능시스템학회 2004 한국지능시스템학회논문지 Vol.14 No.6

본 논문에서는 음성을 본인이 아닌 사람의 음성으로 변환시키기 위하여 피치 변환 기법을 사용한 실시간 음성 변환 방법을 제안하였다. 이러한 목적을 위하여 DFT((Discrete Fourier Transform)를 사용한 표본화율 변환 방법과 SOLA(Synchronized OverLap and Add) 방법을 사용한 시간축 변환 방법을 결합하여 피치를 변환시켰다. 제안된 방법의 성능을 평가하기 위하여 음성 변환 실험을 수행하였다. 실험 결과에서 원 음성 신호는 원 화자의 신원을 알기가 어려운 음성 신호로 바뀌는 것을 알 수 있었다. 제안된 시스템은 시스템의 실시간으로 구현될 수 있는지 확인하기 위하여 TI TMS320C6711DSK 보드를 사용하여 구현되었다. In this paper, real-time voice change method using pitch change technique is proposed to change one's voice to the other voice. For this purpose, sampling rate change method using DFT (Discrete Fourier Transform) method and time scale modification method using SOLA (Synchronized OverLap and Add) method is combined to change pitch. In order to evaluate the performance of the proposed method, voice transformation experiments were conducted. Experimental results showed that original speech signal is changed to the other speech signal in which original speaker's identity is difficult to find. The system is implemented using TI TMS320C6711DSK board to verify the system runs in real time

강인한 음성 인식 시스템을 사용한 감정 인식

김원구(Weon-Goo Kim) 한국지능시스템학회 2008 한국지능시스템학회논문지 Vol.18 No.5

본 논문은 음성을 사용한 인간의 감정 인식 시스템의 성능을 향상시키기 위하여 감정 변화에 강인한 음성 인식 시스템과 결합된 감정 인식 시스템에 관하여 연구하였다. 이를 위하여 우선 다양한 감정이 포함된 음성 데이터베이스를 사용하여 감정 변화가 음성 인식 시스템의 성능에 미치는 영향에 관한 연구와 감정 변화의 영향을 적게 받는 음성 인식 시스템을 구현하였다. 감정 인식은 음성 인식의 결과에 따라 입력 문정에 대한 각각의 감정 모델을 비교하여 입력 음성에 대한 최종감정 인식을 수행한다. 실험 결과에서 강인한 음성 인식 시스템은 음성 파라메터로 RASTA 멜 켑스트럼과 델타 켑스트럼을 사용하고 신호편의 제거 방법으로 CMS를 사용한 HMM 기반의 화자독립 단어 인식기를 사용하였다. 이러한 음성 인식기와 결함된 감정 인식을 수행한 결과 감정 인식기만을 사용한 경우보다 좋은 성능을 나타내었다. This paper studied the emotion recognition system combined with robust speech recognition system in order to improve the performance of emotion recognition system. For this purpose, the effect of emotional variation on the speech recognition system and robust feature parameters of speech recognition system were studied using speech database containing various emotions. Final emotion recognition is processed using the input utterance and its emotional model according to the result of speech recognition. In the experiment, robust speech recognition system is HMM based speaker independent word recognizer using RASTA mel-cepstral coefficient and its derivatives and cepstral mean subtraction(CMS) as a signal bias removal Experimental results showed that emotion recognizer combined with speech recognition system showed better performance than emotion recognizer alone.

확률적 매칭 방법을 사용한 음소열 기반 음성 인식

김원구(Weon-Goo Kim) 한국지능시스템학회 2007 한국지능시스템학회논문지 Vol.17 No.5

본 논문에서는 화자 독립 음소 인식기를 사용하는 음소열 기반 음성 인식 시스템의 성능을 향상시키는 새로운 방법을 제안하였다. 화자독립 음소 HMM을 사용하는 음성 인식 시스템은 입력 문장에 대한 음소열만을 사용하므로 저장 공간은 크게 줄일 수 있다. 그러나 시스템의 성능은 화자독립 모델을 사용하므로 발생하는 음소 오차 때문에 화자 종속 시스템보다 저하된다. 여기에서는 화자 적응 기술을 사용하여 화자독립 모델과 학습 데이터 간의 불일치를 감소시키도록 음소열과 변환 벡터를 반복적으로 추정하는 학습 방법을 제안하였다. 화자 적응을 위한 변환 벡터를 추정하기 위하여 확률적 매칭 방법이 사용되었다. 실험은 전화선을 통하여 얻어진 데이터를 사용한 실험에서 기존 방법에 비하여 약 45%정도 오차가 감소되었다. A new method that improves the performance of the phonetic transcription based speech recognition system is presented with the speaker-independent phonetic recognizer. Since SI phoneme HMM based speech recognition system uses only the phoneme transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the speaker dependent system due to the phoneme recognition errors generated from using SI models. A new training method that iteratively estimates the phonetic transcription and transformation vectors is presented to reduce the mismatch between the training utterances and a set of SI models using speaker adaptation techniques. For speaker adaptation the stochastic matching methods are used to estimate the transformation vectors. The experiments performed over actual telephone line shows that a reduction of about 45% in the error rates could be achieved as compared to the conventional method

확률적 매칭을 사용한 음성 다이얼링 시스템

김원구(Weon-Goo Kim) 한국지능시스템학회 2004 한국지능시스템학회 학술발표 논문집 Vol.14 No.1

This paper presents a method that improves the performance of the personal voice dialling system in which speaker independent phoneme HMM's are used. Since the speaker independent phoneme HMM based voice dialing system uses only the phone transcription of the input sentence, the storage space could be reduced greatly. However, the performance of the system is worse than that of the system which uses the speaker dependent models due to the phone recognition errors generated when the speaker independent models are used. In order to solve this problem, a new method that jointly estimates transformation vectors for the speaker adaptation and transcriptions from training utterances is presented. The biases and transcriptions are estimated iteratively from the training data of each user with maximum likelihood approach to the stochastic matching using speaker-independent phone models. Experimental result shows that the proposed method is superior to the conventional method which used transcriptions only.