Automatic phrase break and sentence stress prediction in English with RNN : RNN 기반의 휴지부 및 문장 강세 자동 예측

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dc.contributor.authorEvgenia Nedelko-
dc.description학위논문 (석사)-- 서울대학교 대학원 : 인문대학 협동과정 인지과학전공, 2018. 8. 정민화.-
dc.description.abstractProsody has been widely researched and studied from different angles in recent years. In linguistics, prosody is concerned with those elements of speech that are not individual phonetic segments (vowels and consonants) but are properties of syllables and larger units of speech. These contribute to linguistic functions such as intonation, tone, stress, and rhythm. Prosody may reflect various features of the speaker or the utterance: the emotional state of the speaker-
dc.description.abstractthe form of the utterance (statement, question, or command)-
dc.description.abstractemphasis, contrast, and focus-
dc.description.abstractor other elements of language that may not be encoded by grammar or by vocabulary.

In the present research we concentrated on such prosodic phenomena like phrase breaks and sentence stress. In English, pausing is more likely before a word carrying a high information content. Defining pause is not easy. First of all, pausing is a very natural phenomenon related to breathing. Also it seems necessary to distinguish between silent pauses and "filled" pauses where a hesitation is perceived but the speaker continues to emit sound.

Prosodic stress, or sentence stress, refers to stress patterns that apply at a higher level than the individual word – namely within a prosodic unit. It may involve a certain natural stress pattern characteristic of a given language, but may also involve the placing of emphasis on particular words because of their relative importance.

The goal of the present work was to conduct a multi-class prediction task consisting of three classes: NB standing for not pause occurring, B standing for a minor pause occurring in the sentence and BB standing for a pause marking a sentence boundary.

The second goal was to conduct a sentence stress prediction task and demonstrate that the implementation of neural network models without additionally extracted features will allow to achieve a relatively high performance. Both prediction tasks were performed based on the word embedding model together with neural network architectures.

The main hypothesis was that the implementation of bidirectional neural networks will help increase the accuracy of pause prediction and drastically improve the overall performance. The second hypothesis was that a pre-trained word embedding model in combination with a neural network architecture will allow to achieve good performance on sentence stress prediction task.

The third and the last goal was to examine and compare the performance of different neural network models on the prediction tasks mentioned above.

Keywords: Prosody prediction, Phrase breaks, Sentence stress, Deep learning, Neural networks.
dc.description.tableofcontentsCHAPTER 1. INTRODUCTION 9

1.1. Motivation & General Description 9

1.2. Purpose of Research 11

1.3. Scope of Research 12

1.4. Structure of the Thesis 12


2.1. Prosody: linguistic theory 13

2.1.1. Pauses and impact on intonation 14

2.1.2. Sentence stress and impact on intonation 17

2.2. Prosody prediction 19

2.2.1. Computational Approaches to Prosody Prediction 19

2.2.2. Phrase break prediction task 21

2.2.3. Sentence stress prediction task 22

2.3. Neural Network Language Models 23

2.4. Word Embeddings 25

2.5. DNN, RNN, LSTM and GRU 26


3.1. Corpus 33

3.2. Approach 35

3.2.1. POS-tagger 36

3.2.2. Activation Functions 37

3.2.3. Dropout 41

3.3. Automatic phrase break prediction 44

3.4. Automatic sentence stress prediction 44

3.5. Experimental Results 45

3.5.1 Phrase break experiment 45

3.5.2 Sentence stress experiment 51

3.6. Discussion 54

Chapter 4. Conclusion 59

4.1. Summary 59

4.2. Contribution 61

4.3. Future work 62
dc.publisher서울대학교 대학원-
dc.titleAutomatic phrase break and sentence stress prediction in English with RNN-
dc.title.alternativeRNN 기반의 휴지부 및 문장 강세 자동 예측-
dc.contributor.affiliation인문대학 협동과정 인지과학전공-
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College of Humanities (인문대학)Program in Cognitive Science (협동과정-인지과학전공)Theses (Master's Degree_협동과정-인지과학전공)
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