• KIPS Transactions on Software and Data Engineering
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• v.12 no.10
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• pp.461-470
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• 2023

While speech animation generation employing deep learning has been actively researched for English, there has been no prior work for Korean. Given the fact, this paper for the very first time employs supervised deep learning to generate Korean speech animation. By doing so, we find out the significant effect of deep learning being able to make speech animation research come down to speech recognition research which is the predominating technique. Also, we study the way to make best use of the effect for Korean speech animation generation. The effect can contribute to efficiently and efficaciously revitalizing the recently inactive Korean speech animation research, by clarifying the top priority research target. This paper performs this process: (i) it chooses blendshape animation technique, (ii) implements the deep-learning model in the master-servant pipeline of the automatic speech recognition (ASR) module and the facial action coding (FAC) module, (iii) makes Korean speech facial motion capture dataset, (iv) prepares two comparison deep learning models (one model adopts the English ASR module, the other model adopts the Korean ASR module, however both models adopt the same basic structure for their FAC modules), and (v) train the FAC modules of both models dependently on their ASR modules. The user study demonstrates that the model which adopts the Korean ASR module and dependently trains its FAC module (getting 4.2/5.0 points) generates decisively much more natural Korean speech animations than the model which adopts the English ASR module and dependently trains its FAC module (getting 2.7/5.0 points). The result confirms the aforementioned effect showing that the quality of the Korean speech animation comes down to the accuracy of Korean ASR.

• Journal of the Korea Computer Graphics Society
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• v.26 no.3
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• pp.49-59
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• 2020

In this paper, we propose a speech animation synthesis specialized in Korean through a rule-based co-articulation model. Speech animation has been widely used in the cultural industry, such as movies, animations, and games that require natural and realistic motion. Because the technique for audio driven speech animation has been mainly developed for English, however, the animation results for domestic content are often visually very unnatural. For example, dubbing of a voice actor is played with no mouth motion at all or with an unsynchronized looping of simple mouth shapes at best. Although there are language-independent speech animation models, which are not specialized in Korean, they are yet to ensure the quality to be utilized in a domestic content production. Therefore, we propose a natural speech animation synthesis method that reflects the linguistic characteristics of Korean driven by an input audio and text. Reflecting the features that vowels mostly determine the mouth shape in Korean, a coarticulation model separating lips and the tongue has been defined to solve the previous problem of lip distortion and occasional missing of some phoneme characteristics. Our model also reflects the differences in prosodic features for improved dynamics in speech animation. Through user studies, we verify that the proposed model can synthesize natural speech animation.

• Journal of KIISE:Computer Systems and Theory
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• v.26 no.9
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• pp.1031-1041
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• 1999

기존의 한글에 대한 입술 애니메이션 방법은 음소의 입모양을 몇 개의 입모양으로 정의하고 이들을 보간하여 입술을 애니메이션하였다. 하지만 발음하는 동안의 실제 입술 움직임은 선형함수나 단순한 비선형함수가 아니기 때문에 보간방법에 의해 중간 움직임을 생성하는 방법으로는 음소의 입술 움직임을 효과적으로 생성할 수 없다. 또 이 방법은 동시조음도 고려하지 않아 음소들간에 변화하는 입술 움직임도 표현할 수 없었다. 본 논문에서는 동시조음을 고려하여 한글을 자연스럽게 발음하는 입술 애니메이션 방법을 제안한다. 비디오 카메라로 발음하는 동안의 음소의 움직임들을 측정하고 입술 움직임 제어 파라미터들을 추출한다. 각각의 제어 파라미터들은 L fqvist의 스피치 생성 제스처 이론(speech production gesture theory)을 이용하여 실제 음소의 입술 움직임에 근사한 움직임인 지배함수(dominance function)들로 정의되고 입술 움직임을 애니메이션할 때 사용된다. 또, 각 지배함수들은 혼합함수(blending function)와 반음절에 의한 한글 합성 규칙을 사용하여 결합하고 동시조음이 적용된 한글을 발음하게 된다. 따라서 스피치 생성 제스처 이론을 이용하여 입술 움직임 모델을 구현한 방법은 기존의 보간에 의해 중간 움직임을 생성한 방법보다 실제 움직임에 근사한 움직임을 생성하고 동시조음도 고려한 움직임을 보여준다.Abstract The existing lip animation method of Hangul classifies the shape of lips with a few shapes and implements the lip animation with interpolating them. However it doesn't represent natural lip animation because the function of the real motion of lips, during articulation, isn't linear or simple non-linear function. It doesn't also represent the motion of lips varying among phonemes because it doesn't consider coarticulation. In this paper we present a new coarticulation model for the natural lip animation of Hangul. Using two video cameras, we film the speaker's lips and extract the lip control parameters. Each lip control parameter is defined as dominance function by using L fqvist's speech production gesture theory. This dominance function approximates to the real lip animation of a phoneme during articulation of one and is used when lip animation is implemented. Each dominance function combines into blending function by using Hangul composition rule based on demi-syllable. Then the lip animation of our coarticulation model represents natural motion of lips. Therefore our coarticulation model approximates to real lip motion rather than the existing model and represents the natural lip motion considered coarticulation.