| dc.contributor.advisor | 萬依萍 | zh_TW |
| dc.contributor.advisor | Wan, I Ping | en_US |
| dc.contributor.author (Authors) | 錢昱夫 | zh_TW |
| dc.contributor.author (Authors) | Chien, Yu Fu | en_US |
| dc.creator (作者) | 錢昱夫 | zh_TW |
| dc.creator (作者) | Chien, Yu Fu | en_US |
| dc.date (日期) | 2010 | en_US |
| dc.date.accessioned | 5-Oct-2011 14:25:45 (UTC+8) | - |
| dc.date.available | 5-Oct-2011 14:25:45 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-Oct-2011 14:25:45 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0097555004 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/51158 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 語言學研究所 | zh_TW |
| dc.description (描述) | 97555004 | zh_TW |
| dc.description (描述) | 99 | zh_TW |
| dc.description.abstract (摘要) | 論文提要內容：（共一冊，17770字，分六章） 本研究用雙音節詞來探討不同音段和聲調在臺灣華語的口語詞彙辨識歷程中的重要性。Cohort模型（1978）非常強調詞首訊息的重要性，然而Merge模型（2000）認為訊息輸入和音韻表徵的整體吻合才是最重要的。因此，本研究企圖探索不同音段和詞首詞尾在臺灣華語的口語詞彙辨識歷程中的重要性。然而，聲調的問題並無在先前的模型裡被討論。因此，聲調在臺灣華語的口語詞彙辨識歷程中所扮演的角色也會在本研究中被討論。另外，詞頻效應也會在本研究中被探索。本研究的三個實驗均由同樣的十五名受試者參加。實驗一是測試不同音段在臺灣華語的口語詞彙辨識歷程中的重要性。實驗一操弄十二個雙音節高頻詞和十二個雙音節低頻詞，每一個雙音節詞的每一個音段都分別被噪音擋住。實驗二是在探索詞首和詞尾在臺灣華語的口語詞彙辨識歷程中的重要性。實驗二操弄十二個雙音節高頻詞和十二個雙音節低頻詞。這些雙音節詞的詞首CV或詞尾VG/N都分別被雜音擋住。實驗三操弄二十四個雙音節高頻詞和二十四個雙音節低頻詞。這些雙音節詞的聲調都被拉平到100赫茲。在這三個實驗中，受試者必須聽這些被操弄過的雙音節詞，並且辨認它們。受試者的反應時間和辨詞的準確率都用E-Prime來記錄。實驗結果顯示，傳統的Cohort模型不能被完全支持，因為詞首訊息被噪音擋住的詞仍能被受試者成功的辨識出來。強調聲音訊息和音韻表徵的整體吻合度的Merge模型，比較能解釋實驗的結果。然而，Merge模型必須要加入韻律節點才能處理臺灣華語的聲調辨識的問題。本研究也顯示，雙音節詞的第一個音節的母音在口語詞彙辨識歷程中是最重要的，而雙音節詞的第二個音節的母音是第二重要的。這是因為母音帶了最多訊息，包括聲調。另外，雙音節詞的詞首和詞尾在臺灣華語的口語詞彙辨識歷程中是扮演差不多重要的角色。母音對於聲調的感知是最重要的。詞頻效應也完全表現在臺灣華語的口語詞彙辨識歷程中。關鍵詞：口語詞彙辨識歷程、臺灣華語、華語聲調、音段、Cohort模型、Merge模型 | zh_TW |
| dc.description.abstract (摘要) | The present study investigated the importance of different segments and the importance of tone in spoken word recognition in Taiwan Mandarin by using isolated disyllabic words. Cohort model (1978) emphasized the absolute importance of the initial information. On the contrary, Merge (2000) proposed that the overall match between the input and the phonological representation is the most crucial. Therefore, this study tried to investigate the importance of different segments and the importance of onsets and offsets in the processing of Mandarin spoken words. However, the issues of tone were not included in the previous models. Thus, the importance of tone was also investigated in this study. The issues about frequency effect were also explored here. Three experiments were designed in this study. Fifteen subjects were invited to participate in all three experiments. Experiment 1 was designed to investigate the importance of different segments in Taiwan Mandarin. In experiment 1, 12 high-frequency disyllabic words and 12 low-frequency disyllabic words were selected. Each segment of each disyllabic word was replaced by the hiccup noise. Experiment 2 was designed to investigate the importance of onsets and offsets. In experiment 2, 12 high-frequency disyllabic words and 12 low-frequency disyllabic words were chosen. The CV of the first syllable and the VG/N of the second syllable were replaced by the hiccup noise. Experiment 3 was designed to investigate the importance of Mandarin tones. In experiment 3, 24 high-frequency disyllabic words and 24 low-frequency disyllabic words were selected. The tones of the disyllabic words were leveled to 100 Hz. In the three experiments, subjects listened to the stimuli and recognized them. The reaction time and accuracy were measured by E-Prime. The results indicated that traditional Cohort model cannot be fully supported because words can still be correctly recognized when word initial information is disruptive. Merge model, which proposed that the overall match between the input and the lexical representation is the most important, was more compatible with the results here. However, Merge model needs to include the prosody nodes, so that it can account for the processing of tones in Taiwan Mandarin. In addition, the current study also showed that the first vowel of the disyllabic word is the most crucial and the second vowel of the disyllabic word is the second influential since the vowel carries the most important information, including tones. The results of experiment 2 demonstrated that the onsets and offsets are almost the same important in Mandarin. Furthermore, vowel is the most influential segment for the perception of Mandarin tones. Finally, frequency effect appeared in the processing of Mandarin words. Keywords: spoken word recognition, Taiwan Mandarin, Mandarin tones, segments, Cohort, Merge | en_US |
| dc.description.tableofcontents | CHAPTER 1 INTRODUCTION……………………………………………………...11.1 The background of spoken word recognition………………………………...11.2 Motivation and research questions…………………………………………...41.3 Organization………………………………………………………………….9CHAPTER 2 LITERATURE REVIEW……………………………………………...10 2.1 Models of spoken word recognition………………………………………...10 2.1.1 Cohort model (1980)…………………………………………….......10 2.1.2 Merge (2000)………………………………………………………...13 2.2 The role of acoustic onsets and offsets……………………………………...15 2.3 Mandarin phonological system……………………………………………..20 2.4 The acoustic-phonetic cues of the consonants in Taiwan Mandarin………..20 2.4.1 The acoustic-phonetic cues of stops…………………………………21 2.4.2 The acoustic-phonetic cues of nasals………………………………..22 2.4.3 The acoustic-phonetic cues of fricatives…………………………….22 2.4.4 The acoustic-phonetic cues of affricates…………………………….25 2.5 The acoustic-phonetic cues of the vowels in Taiwan Mandarin…………….25 2.6 Mandarin tone………………………………………………………………25 2.6.1The perception of Mandarin Chinese tones…………………………..25 2.6.2 The processing of Mandarin tone……………………………………29 2.7 Summary……………………………………………………………………31CHAPTER 3 METHODS……………………………………………………………33 3.1 Subjects……………………………………………………………………..33 3.2 Equipment…………………………………………………………………..33 3.3 Stimuli………………………………………………………………………33 3.3.1 Word frequencies…………………………………………………….34 3.3.2 Segmentation………………………………………………………...34 3.3.2.1 Segmentation of the initial consonant………………………..35 3.3.2.2 Segmentation of prenuclear glides…………………………...36 3.3.2.3 Segmentation of vowels……………………………………...39 3.3.2.4 Segmentation of postnuclear glides, and final nasals………...41 3.3.3 The leveling of tones………………………………………………...42 3.4 Design……………………………………………………………………….43 3.5 Procedures…………………………………………………………………..46CHAPTER 4 RESULTS……………………………………………………………...48 4.1 Experiment 1: one-segment disruption...........................................................48 4.2 Experiment 2: two-segment disruption...…………………………….……..53 4.3 Experiment 3: tone leveling...........................................................................60CHAPTER 5 DISCUSSION…………………………………………………………64 5.1 The results and the two models (Cohort and Merge).....................................64 5.2 Cohort and Merge models in Taiwan Mandarin…………………………….66 5.3 Merge model: Spoken word recognition in Taiwan Mandarin.......................70CHAPTER 6 CONCLUSION………………………………………………………..74REFERENCES……………………………………………………………………….77APPENDIX 1………………………………………………………………………...79APPENDIX 2………………………………………………………………………...81 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0097555004 | en_US |
| dc.subject (關鍵詞) | 口語詞彙辨識歷程 | zh_TW |
| dc.subject (關鍵詞) | 臺灣華語 | zh_TW |
| dc.subject (關鍵詞) | 華語聲調 | zh_TW |
| dc.subject (關鍵詞) | 音段 | zh_TW |
| dc.subject (關鍵詞) | Cohort模型 | zh_TW |
| dc.subject (關鍵詞) | Merge模型 | zh_TW |
| dc.subject (關鍵詞) | spoken word recognition | en_US |
| dc.subject (關鍵詞) | Taiwan Mandarin | en_US |
| dc.subject (關鍵詞) | Mandarin tones | en_US |
| dc.subject (關鍵詞) | segments | en_US |
| dc.subject (關鍵詞) | Cohort | en_US |
| dc.subject (關鍵詞) | Merge | en_US |
| dc.title (題名) | 臺灣華語的口語詞彙辨識歷程: 從雙音節詞來看 | zh_TW |
| dc.title (題名) | Spoken word recognition in Taiwan Mandarin: evidence from isolated disyllabic words | en_US |
| dc.type (資料類型) | thesis | en |
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