• 음성과학
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• 제8권4호
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• pp.207-214
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• 2001

This study describes the focus in nine structure types of English relative clauses (SS, SO, SP, PS, PO, PP, OS, OO, OP), classified according to the grammatical role of both the head that the relative clause modifies and the gap within the relative clause. The informants for this study are 2 middle school students, 4 high school students in four formal classroom in Korea and 2 native speakers. To obtain the accurate intonation patterns, Visi-Pitch II Model 3300 was used for data analyses. Major findings are as follows: (1) The feature of the intonation in English relative clauses showed prosodic prominence at the head, but the English learners in Korea didn't show prosodic prominence; (2) the fact that all heads have prosodic prominence says that the head in relative clauses has prosodic focus; (3) in the fact that the English learners have flat pitch in the whole sentences, the problem of intonation education is found out.

• 말소리와 음성과학
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• 제6권4호
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• pp.37-45
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• 2014

Korean wh-questions and yes-no questions have morphologically the same structure. In speech, however, two types of questions are distinguished by prosodic difference. In this study, we examined if Korean native speakers can distinguish wh-question and yes-no questions produced by Chinese Korean leaners based on the prosodic information contained in the sentences. For this purpose, we performed perception analysis, and 15 Korean native speakers participated in the perception test. The results show that two types of interrogative sentences produced by Chinese Korean leaners were not distinguished by constant pitch contours. These results reveal that Chinese Korean leaners cannot match prosodic meaning and prosodic form. The most saliant prosodic feature used perceptually by native speakers to discriminate two types of interrogative sentences is pitch difference between the F0 pick of wh-word and boundary tone.

• 음성과학
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• 제10권3호
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• pp.133-144
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• 2003

The purpose of this acoustic experimental study was to investigate interrelation between prosodic features of Korean spoken language and those of Korean folk songs. The words of Changbutaryoung were spoken for analysis of spoken language by three female graduate students and the song was sung for musical features by three Kyunggi Minyo singers. Pitch contours were analyzed from sound spectrogram made by Pitch Works. Results showed that special musical voices (breaking, tinkling, vibrating, etc.) and tunes (rising, falling, level, etc) of folk song were discovered at the same place where accents of spoken language came. It appeared that, even though the patterns of pitch contour were different from each other, there was positive interrelation between prosodic features of Korean spoken language and those of Korean folk songs.

• 대한음성학회지:말소리
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• 제61호
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• pp.87-100
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• 2007

A punctuation generation system which combines prosodic information with acoustic and language model information is presented. Experiments have been conducted first for the reference text transcriptions. In these experiments, prosodic information was shown to be more useful than language model information. When these information sources are combined, an F-measure of up to 0.7830 was obtained for adding punctuation to a reference transcription. This method of punctuation generation can also be applied to the 1-best output of a speech recogniser. The 1-best output is first time aligned. Based on the time alignment information, prosodic features are generated. As in the approach applied in the punctuation generation for reference transcriptions, the best sequence of punctuation marks for this 1-best output is found using the prosodic feature model and an language model trained on texts which contain punctuation marks.

• 인지과학
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• 제19권1호
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• pp.89-105
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• 2008

본 논문에서는 자연스러운 한국어 운율구 경계를 예측하기 위해 (1) 문장 성분을 하위범주화하고, (2) 세분화된 문장 성분 간 의존관계를 이용하여 통사구를 추출하며 (3) 추출한 통사구의 유형에 따른 운율구 경계 예측 규칙을 설정하였다. 또한, (4) 통사적 정보 외에도 통사구와 문장의 길이, 통사구의 문장 내 위치, 문맥의 의미 정보 등에 따라 가변적인 운율구 경계를 판단하여 보다 자연스러운 한국어 운율구 경계 예측 시스템을 개발하였다. 그 결과 통사구 경계와 상관관계가 높은 강한 운율구 경계 예측과 운율구 내부 비경계 예측에 있어 90% 이상의 높은 재현율과 정확도를 보였으며, 전체 운율구 경계 예측에 있어서도 87% 이상의 성능을 보였다.

• 한국언어정보학회:학술대회논문집
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• 한국언어정보학회 2007년도 정기학술대회
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• pp.405-414
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• 2007

This paper describes a preliminary work on prosody modeling aspect of a text-to-speech system for Thai. Specifically, the model is designed to predict symbolic markers from text (i.e., prosodic phrase boundaries, accent, and intonation boundaries), and then using these markers to generate pitch, intensity, and durational patterns for the synthesis module of the system. In this paper, a novel method for annotating the prosodic structure of Thai sentences based on dependency representation of syntax is presented. The goal of the annotation process is to predict from text the rhythm of the input sentence when spoken according to its intended meaning. The encoding of the prosodic structure is established by minimizing speech disrhythmy while maintaining the congruency with syntax. That is, each word in the sentence is assigned a prosodic feature called strength dynamic which is based on the dependency representation of syntax. The strength dynamics assigned are then used to obtain rhythmic groupings in terms of a phonological unit called foot. Finally, the foot structure is used to predict the durational pattern of the input sentence. The aforementioned process has been tested on a set of ambiguous sentences, which represents various structural ambiguities involving five types of compounds in Thai.

• 한국정보과학회논문지:소프트웨어및응용
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• 제37권11호
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• pp.837-844
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• 2010

운율구 경계 예측은 대화체 음성합성을 실현하기 위한 주요한 자연언어처리 기술 중 하나이다. 본 논문은 자연스러운 한국어 운율구 경계 예측을 실현하고자 기존의 학습 자질을 대신할 새로운 학습 자질을 제안한다. 이 새로운 자질들은 기존의 학습 자질보다 실제 언어생활에서 운율구 경계 발생에 영향을 미치는 여러 요인을 더 잘 반영한다. 특히, 수작업으로 구축한 운율구 경계 예측 규칙을 이용하여 추출한 학습 자질은 높은 정확도 향상에 이바지한다. 본 논문에서 제안한 새로운 학습 자질을 바탕으로 CRFs(Conditional Random Fields)를 이용하여 운율구 경계 예측 모델을 만들었다. 그 결과 3단계 운율구 경계(강한 경계, 약한 경계, 운율구 내부 비경계) 예측에서 86.63%의 정확도를, 6단계 운율구 경계(상승조/하강조 강한 경계, 상승조/하강조/평탄조 약한 경계, 운율구 내부 비경계) 예측에서는 81.14%의 정확도를 보였다.

• 한국언어정보학회지:언어와정보
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• 제14권2호
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• pp.47-70
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• 2010

This study investigates the semantic and prosodic properties of the so-called contrastive topic. We posit two informational primitives, namely, topical feature [+-T] and focal feature [+-F], from which four different informational categories, i.e., [+T, +F], [+T, -F], [-T, +F], and [-T, -F], are yielded. It is proposed that the informational category of contrastive topic has focal property [+F] as well as topical property [+T]. Based on the semantic approach that regards the function of [+F] as identificational predication and that of [+T] as forming a semantic conditional clause, it is shown that the semantic function of contrastive topic, which is specified as [+T, +F], is the combination of these two functions, i.e., identificational predication in a semantic conditional clause. This is supported by a scrutinized exploration of the prosodic pattern of English contrastive topic.

• 대한음성학회:학술대회논문집
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• 대한음성학회 2003년도 5월 학술대회지
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• pp.56-60
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• 2003

The purpose of this paper is to research voice imitation. Voice imitation changes various phonetic feature. Also, in our experimental results, voice imitation has preferential prosody difference. For imitating voice, imitators change their fundamental frequency bandwidths for the most part. Imitative speakers change their high fundamental frequencies effectively while they maintain their low fundamental frequencies. Also, excellent group is distinctly superior to common group for imitating prosodic patterns. That is, the f0 bandwidth's change and the prosodic patterns are significant in imitating voice. But the low f0 is maintain by all speakers.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 추계학술대회논문집
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• pp.762-766
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• 2002

This paper describes the emotion recognition algorithm using HMM(Hidden Markov Model) method. The relation between the mechanic system and the human has just been unilateral so far. This is the why people don't want to get familiar with multi-service robots of today. If the function of the emotion recognition is granted to the robot system, the concept of the mechanic part will be changed a lot. Pitch and Energy extracted from the human speech are good and important factors to classify the each emotion (neutral, happy, sad and angry etc.), which are called prosodic features. HMM is the powerful and effective theory among several methods to construct the statistical model with characteristic vector which is made up with the mixture of prosodic features