• Proceedings of the KSPS conference
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• 1996.10a
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• pp.574-575
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• 1996

The theme of the current study is to study intonation of Taiwanese(Tw.) by comparing the intonation patterns in native language (Ll), target language (L2), and interlanguage (IL). Studies on interlanguage have dealt primarily with segments. Though there were studies which addressed to the issues of interlanguage intonation, more often than not, they didn't offer evidence for the statement, and the hypotheses were mainly based on impression. Therefore, a formal description of interlanguage intonation is necessary for further development in this field. The basic assumption of this study is that native speakers of one language perceive and produce a second language in ways closely related to the patterns of their first language. Several studies on interlanguage prosody have suggested that prosodic structure and rules are more subject to transfer than certain other phonological phenomena, given their abstract structural nature and generality(Vogel 1991). Broselow(1988) also shows that interlanguage may provide evidence for particular analyses of the native language grammar, which may not be available from the study of the native language alone. Several research questions will be addressed in the current study: A. How does duration vary among native and nominative utterances\ulcorner The results shows that there is a significant difference in duration between the beginning English learners, and the native speakers of American English for all the eleven English sentences. The mean duration shows that the beginning English learners take almost twice as much time (1.70sec.), as Americans (O.97sec.) to produce English sentences. The results also show that American speakers take significant longer time to speak all ten Taiwanese utterances. The mean duration shows that Americans take almost twice as much time (2.24sec.) as adult Taiwanese (1.14sec.) to produce Taiwanese sentences. B. Does proficiency level influence the performance of interlanguage intonation\ulcorner Can native intonation patterns be achieved by a non-native speaker\ulcorner Wenk(1986) considers proficiency level might be a variable which related to the extent of Ll influence. His study showed that beginners do transfer rhythmic features of the Ll and advanced learners can and do succeed in overcoming mother-tongue influence. The current study shows that proficiency level does play a role in the acquisition of English intonation by Taiwanese speakers. The duration and pitch range of the advanced learners are much closer to those of the native American English speakers than the beginners, but even advanced learners still cannot achieve native-like intonation patterns. C. Do Taiwanese have a narrower pitch range in comparison with American English speakers\ulcorner Ross et. al.(1986) suggests that the presence of tone in a language significantly inhibits the unrestricted manipulation of three acoustical measures of prosody which are involved in producing local pitch changes in the fundamental frequency contour during affective signaling. Will the presence of tone in a language inhibit the ability of speakers to modulate intonation\ulcorner The results do show that Taiwanese have a narrower pitch range in comparison with American English speakers. Both advanced (84Hz) and beginning learners (58Hz) of English show a significant narrower FO range than that of Americans' (112Hz), and the difference is greater between the beginning learners' group and native American English speakers.

• Proceedings of the KSPS conference
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• 1997.07a
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• pp.228-229
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• 1997

1 Intonation is important. It cannot be ignored. To convince students of the importance of intonation, we can use sentences with two very different interpretations according to intonation. Example: "I thought it would rain" with a fallon "rain" means it did not rain, but with a fall on "thought" and a rise on "rain" it means that it did rain. 2 Although complex, intonation is structured. For both teacher and student, the big job of tackling intonation is made simpler by remembering that intonation can be analysed into systems and units. There are three main systems in English intonation: Tonality (division into phrases) Tonicity (selection of accented syllables) Tone (the choice of pitch movements) Examples: Tonality: My brother who lives in London is a doctor. Tonicity: Hello. How ARE you. Hello. How are YOU. Tone: Ways to say "Thank you" 3 In deciding what to teach, we must distinguish what is universal from what is specifically English. This is where contrastive studies of intonation are very valuable. Usually, for instance, division into phrases (tonality) works in broadly similar ways across languages. Some uses of pitch are also similar across languages - for example, very high pitch may signal excitement or urgency. 4 Although most people think that intonation is mainly about pitch (the tone system), actually accent placement (tonicity) is probably the single most important aspect of English intonation. This is because it is connected with information focus, and the effects on interpretation are very clear-cut. Example: They asked for coffee, so I made them coffee. (The second occurrence of "coffee" must not be accented). 5 Ear-training is the beginning of intonation training in the VeL approach. First, students learn to identify fall vs rise vs fall-rise. To begin with, single words are used, then phrases and sentences. When learning tones, the fIrst words used should have unstressed syllables after the stressed syllable (Saturday) to make the pitch movement clearer. 6 In production drills, the fIrst thing is to establish simple neutral patterns. There should be no drama or really special meanings. Simple drills can be used to teach important patterns: Example: A: Peter likes football B: Yes JOHN likes football TOO A: Mary rides a bike B: Yes JENny rides a bike TOO 7 The teacher must be systematic and let learners KNOW what they are learning. It is no good using new patterns and hoping that students will "pick them up" without noticing. 8 Visual feedback of fundamental frequency with a computer display can help students learn correct patterns. The teacher can use the display to demonstrate patterns, or students can practise by themselves, imitating recorded models.

• Radiation Oncology Journal
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• v.23 no.1
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• pp.17-21
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• 2005

Purpose : To evaluate the voices of irradiated patients with early glottic carcinoma and to compare these with the voices of healthy volunteers. Materials and Methods : The voice samples (sustained vowel) of seventeen male patients who had been irradiated for T1a glottic squamous carcinoma at least 1 year prior to the study were analyzed with objective voice analyzer (acoustic voice analysis, aerodynamic test, and videostroboscopic analysis) and compared with those of a normal group of twenty age- and sex-matched volunteers. Average fundamental frequency, jitter, shimmer, and noise-to-harmonic ratio were obtained for acoustic voice analysis. Maximal phonation time, mean flow rate, intensity, subglottic pressure, glottal resistance, glottal efficiency, and glottal power were obtained for aerodynamic test. Results : The irradiated group presented higher values of shimmer in acoustic voice analysis. There was no significant difference between two groups in other parameters. Conclusion : In this study all the objective voice parameters except shimmer were no4 significantly different between the irradiated group and the control group. These results suggest that the voice quality is minimally affected by radiation therapy for 71 a glottic carcinoma.

• Proceedings of the KSPS conference
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• 1996.10a
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• pp.246-246
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• 1996

Purpose: Some of the properties of the prosodic phrasing and some acoustic and phonological effects of contrastive focus on the tonal pattern of Seoul Korean is explored based on a brief experiment of analyzing the fundamental frequency(=FO) contour of the speech of the author. Data Base and Analysis Procedures: The examples were chosen to contain mostly nasal and liquid consonants, since it is difficult to track down the formants in stops and fricatives during their corresponding consonantal intervals and stops may yield an effect of unwanted increase in the FO value due to their burst into the following vowel. All examples were recorded three times and the spectrum of the most stable repetition was generated, from which the FO contour of each sentence was obtained, the peaks with a value higher than 250Hz being interpreted as a high tone (=H). The result is then discussed within the prosodic hierarchy framework of Selkirk (1986) and compared with the tonal pattern of the Northern Kyungsang dialect of Korean reported in Kenstowicz & Sohn (1996). Prosodic Phrasing: In N.K. Korean, H never appears both on the object and on the verb in a neutral sentence, which indicates the object and the verb form a single Phonological Phrase ($={\phi}$), given that there is only one pitch peak for each $={\phi}$. However, Seoul Korean shows that both the object and the verb have H of their own, indicating that they are not contained in one $={\phi}$. This violates the Optimality constraint of Wrap-XP (=Enclose a lexical head and its arguments in one $={\phi}$), while N.K. Korean obeys the constraint by grouping a VP in a single $={\phi}$. This asymmetry can be resolved through a constraint that favors the separate grouping of each lexical category and is ranked higher than Wrap-XP in Seoul Korean but vice versa in N.K. Korean; $Align-x^{lex}$ (=Align the left edge of a lexical category with that of a $={\phi}$). (1) nuna-ka manll-ll mEk-nIn-ta ('sister-NOM garlic-ACC eat-PRES-DECL') a. (LLH) (LLH) (HLL) ----Seoul Korean b. (LLH) (LLL LHL) ----N.K. Korean Focus and Phrasing: Two major effects of contrastive focus on phonological phrasing are found in Seoul Korean: (a) the peak of an Intonatioanl Phrase (=IP) falls on the focused element; and (b) focus has the effect of deleting all the following prosodic structures. A focused element always attracts the peak of IP, showing an increase of approximately 30Hz compared with the peak of a non-focused IP. When a subject is focused, no H appears either on the object or on the verb and a focused object is never followed by a verb with H. The post-focus deletion of prosodic boundaries is forced through the interaction of StressFocus (=If F is a focus and DF is its semantic domain, the highest prominence in DF will be within F) and Rightmost-IP (=The peak of an IP projects from the rightmost $={\phi}$). First Stress-F requires the peak of IP to fall on the focused element. Then to avoid violating Rightmost-IP, all the boundaries after the focused element should delete, minimizing the number of $={\phi}$'s intervening from the right edge of IP. (2) (omitted) Conclusion: In general, there seems to be no direct alignment constraints between the syntactically focused element and the edge of $={\phi}$ determined in phonology; all the alignment effects come from a single requirement that the peak of IP projects from the rightmost $={\phi}$ as proposed in Truckenbrodt (1995).

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.59-83
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• 2018

With the increasing importance of sentiment analysis to grasp the needs of customers and the public, various types of deep learning models have been actively applied to English texts. In the sentiment analysis of English texts by deep learning, natural language sentences included in training and test datasets are usually converted into sequences of word vectors before being entered into the deep learning models. In this case, word vectors generally refer to vector representations of words obtained through splitting a sentence by space characters. There are several ways to derive word vectors, one of which is Word2Vec used for producing the 300 dimensional Google word vectors from about 100 billion words of Google News data. They have been widely used in the studies of sentiment analysis of reviews from various fields such as restaurants, movies, laptops, cameras, etc. Unlike English, morpheme plays an essential role in sentiment analysis and sentence structure analysis in Korean, which is a typical agglutinative language with developed postpositions and endings. A morpheme can be defined as the smallest meaningful unit of a language, and a word consists of one or more morphemes. For example, for a word '예쁘고', the morphemes are '예쁘(= adjective)' and '고(=connective ending)'. Reflecting the significance of Korean morphemes, it seems reasonable to adopt the morphemes as a basic unit in Korean sentiment analysis. Therefore, in this study, we use 'morpheme vector' as an input to a deep learning model rather than 'word vector' which is mainly used in English text. The morpheme vector refers to a vector representation for the morpheme and can be derived by applying an existent word vector derivation mechanism to the sentences divided into constituent morphemes. By the way, here come some questions as follows. What is the desirable range of POS(Part-Of-Speech) tags when deriving morpheme vectors for improving the classification accuracy of a deep learning model? Is it proper to apply a typical word vector model which primarily relies on the form of words to Korean with a high homonym ratio? Will the text preprocessing such as correcting spelling or spacing errors affect the classification accuracy, especially when drawing morpheme vectors from Korean product reviews with a lot of grammatical mistakes and variations? We seek to find empirical answers to these fundamental issues, which may be encountered first when applying various deep learning models to Korean texts. As a starting point, we summarized these issues as three central research questions as follows. First, which is better effective, to use morpheme vectors from grammatically correct texts of other domain than the analysis target, or to use morpheme vectors from considerably ungrammatical texts of the same domain, as the initial input of a deep learning model? Second, what is an appropriate morpheme vector derivation method for Korean regarding the range of POS tags, homonym, text preprocessing, minimum frequency? Third, can we get a satisfactory level of classification accuracy when applying deep learning to Korean sentiment analysis? As an approach to these research questions, we generate various types of morpheme vectors reflecting the research questions and then compare the classification accuracy through a non-static CNN(Convolutional Neural Network) model taking in the morpheme vectors. As for training and test datasets, Naver Shopping's 17,260 cosmetics product reviews are used. To derive morpheme vectors, we use data from the same domain as the target one and data from other domain; Naver shopping's about 2 million cosmetics product reviews and 520,000 Naver News data arguably corresponding to Google's News data. The six primary sets of morpheme vectors constructed in this study differ in terms of the following three criteria. First, they come from two types of data source; Naver news of high grammatical correctness and Naver shopping's cosmetics product reviews of low grammatical correctness. Second, they are distinguished in the degree of data preprocessing, namely, only splitting sentences or up to additional spelling and spacing corrections after sentence separation. Third, they vary concerning the form of input fed into a word vector model; whether the morphemes themselves are entered into a word vector model or with their POS tags attached. The morpheme vectors further vary depending on the consideration range of POS tags, the minimum frequency of morphemes included, and the random initialization range. All morpheme vectors are derived through CBOW(Continuous Bag-Of-Words) model with the context window 5 and the vector dimension 300. It seems that utilizing the same domain text even with a lower degree of grammatical correctness, performing spelling and spacing corrections as well as sentence splitting, and incorporating morphemes of any POS tags including incomprehensible category lead to the better classification accuracy. The POS tag attachment, which is devised for the high proportion of homonyms in Korean, and the minimum frequency standard for the morpheme to be included seem not to have any definite influence on the classification accuracy.