Word Pronunciation Text To Voice
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Word Pronunciation Text To Voice

09 zganec si pron pronunciation lexicon a new language resource..
448 Informatica

(2006) 447452

Gros et al.The Pronunciation Lexicon Markup Language, based on PLS, is designed to allow open, portable specification of pronunciation information for speech recognition and speech synthesis engines.The language is intended to be easy to use by developers while supporting the accurate specification of pronunciation information for international use.

The LC-STAR project consortium published another set of recommendations for speech technology lexicons, with an emphasis on application in machine translation, speech recognition and speech synthesis (Shamas & van den Heuvel, 2004; Ferse et al., 2004).A Slovenian lexicon, produced at the University of Maribor, has been built in the scope of the project (Verdonik et al., 2004).Compared to the LC-STAR lexicon specifications the current version of PLS lacks description specifications for more complex features, such as morphological, syntactic, and semantic features of

lexical entries.In Slovenian, lexical stress can be located on almost any syllable and it obeys hardly any rules.The stressed syllable in Slovenian may form the ultimate, the penultimate or the preantepenultimate syllable of a polysyllabic word.Speakers of Slovenian have to learn lexical stress positions along with learning the language.As a consequence, a pronunciation lexicon that indicates lexical stress positions for as many Slovenian words as possible is crucial for the development of speech technology applications and linguistic research.

Such a lexicon can be used either in its full-blown form or as a training material for machine learning techniques aimed at automatically predicting word pronunciations.Several attempts towards pronunciation lexicon construction for Slovenian have been reported so far (Derli & Ka, 1997; Gros & Miheli, 1999; Gros et al., 2001; ef et al., 2002; Verdonik et al., 2002; Miheli et al., 2003).However, none of them has used the full lemma set as given in the Dictionary of Standard Slovenian (SSKJ) (SSKJ, 1991).

The paper describes the construction of a comprehensive reference pronunciation lexicon for Slovenian based on two sources: the information from the SSKJ and another list of the most frequent inflected word forms, which has been derived by an analysis of contemporary Slovenian text corpora.

The work on designing a new pronunciation lexicon begins with the selection of words, multi-word expressions or phrases, which will be represented in the lexicon.Several word-list selection procedures are known (Ziegenheim, 2003).The construction of the SI-PRON lexicon started with the complete lemma word list of 93,154 entries from the SSKJ provided by the Fran Ramov Institute of the Slovenian Language, furnished with basic lexical stress information on the stressed vowels and pronunciation exceptions.

The complete word pronunciations still had to be determined.In order to further expand the SI-PRON word list, we are augmenting the SSKJ lemma descriptions with part-of-speech information and declension/conjugation categories (Toporii, 1991), specifying the inflectional paradigms of the lemmas.Irregular inflected word forms are processed separately.Using automatic procedures, we are fully expanding the lemmas into inflected word forms.So far, over 1 million lexemes containing lexical stress information have been derived.Since SSKJ contains many words derived from literary texts, not so common in everyday situations, we decided to upgrade the SI-PRON pronunciation lexicon with a list of 50,000 most frequent inflected word forms whose lemmas are not covered by the SSKJ word list.

This additional word list has been derived from a statistical analysis of a contemporary Slovenian text corpus.The corpus comprising over 3 million Slovenian words was composed mainly from fiction and mainstream Slovenian newspaper texts: Delo, Veer, and the former Slovenec.After tokenization and the elimination of numerals, named entities, acronyms, and abbreviations, the remaining text corpus included over 3 million tokens.Acronyms, abbreviations, and named entities were stored into separate word lists.

A statistical analysis performed on the text corpus showed that about 50.000 most frequent words accounted for approaching 95% of all non-SSKJ words used in the text corpus (Gros & Miheli, 1999).These words form the main additional word list.They were equipped with part-of-speech tags indicating the part-of-speech function of the words in the text corpus.Collocations and Multi-word The identification of collocations, i.e.current combinations of words as they appear in context, can considerably increase the naturalness of synthetic speech.

In human speech, collocations act as prosodic units and are subject to a higher degree of reduction and internal coarticulation than they would be had they been ordinary, separate words.We have chosen a lexical approach for handling collocations.The most common collocations or multi-word expressions, reflexive verbs included, are stored in a separate pronunciation lexicon.Phonetic Transcriptions We have developed a tool to automatically derive word pronunciations for the SSKJ inflected words, by looking-up their stem pronunciation and appending that of the correct inflection from inflectional paradigms and morphological rules of Slovenian (Toporii, 1991).

Therefore, the pronunciation of lexemes has been derived automatically for the SSKJ and SSKJ inflected word lists (about 2,500 entries, mainly words of foreign origin that do not obey the general Slovenian pronunciation rules, have been manually transcribed), and semi-automatically for the remaining part of the word list.Automatic lexical stress assignment and automatic 450 Informatica

(2006) 447452

Gros et al
word pronunciation text to voice
Improving Pronunciation Via Accent Reduction And Text-to …
Improving Pronunciation via Accent Reduction and Text-to-speech Software Ferit Kılıçkaya* *Department of Foreign Language Education, Middle East Technical … (informatica.si)
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Word Prediction/Text to Speech Programs ... volume, voice and pronunciation Speaks letters, words, sentences and paragraphs as you type Reads any text within (j-let.org)
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Voice Rendering Raw text or tagged text Prosodic Analysis ... At the word level, pronunciation dictionaries, combined with morphological decomposition are used. (texthelp.com)
.

for the pronunciation of the Slovenian word: "dober",

meaning "good" in English -- Figure 1.An example of a simple lexicon file with a single
D 097
Form, the instructors administered the pre-test.One class (control group) followed traditional instruction (using a CD player and a pronunciation text-book- Tree or Three?), another class (experimental group 1) followed traditional instruction which integrated the use of accent reduction software (Pronunciation Power I) and the final class (experimental group 2) followed traditional instruction which integrated the use of accent reduction and text-to-speech software (Text Aloud MP3 with NeoSpeech voices- Paul and Kate The sample consisted of 10 students in the control group, 13 students in the experimental group 1, and 12 students in the experimental group 2.The study lasted for 16 weeks and the instructor met the groups three hours each week.

With the results obtained from the test, and by means of a one-way ANOVA test, it was possible to establish whether or not there were significant differences between two groups of participants at the 0.05 alpha level (see Table 1).

Table 1 Pre-test results Group Std.

Deviation Std.

Errorcontrol 10 5.60 experimental1 15 5.73 .118 experimental2 10 5.60 Total 35 5.66

Sum of Squares df Square Groups Within Groups 7.733 32 Total 7.886 34 As can be seen, the significance level was higher than 0.05, (2,32)= .315, which lead to the conclusion that there were no significant differences between the groups.

Once this point became clear, the study was carried out with these three groups.On the last day of class, the instructor administered the post-test to all of the groups.Also, the experimental group 2 was interviewed regarding their views on accent reduction and text-to-speech software.The scores obtained by pre and post tests were statistically analyzed to see whether there was a statistically significant difference among the 3.Data Analysis The post-test scores obtained by experimental and control groups were analyzed using the SPSS software package using the one-way ANOVA test to establish whether there were significant differences among the three groups of participants at the 0.05 alpha levels (see Table 2).

Post-test results Group Std.Deviation Std.

Errorcontrol 62.50 4.859 1.537 experimental170.33 6.651 1.717 experimental278.10 2.283 Total 70.31 7.851 1.327

Sum of Squaresdf Square Groups 1216.810608.405 22.156Within Groups878.73332 27.460 Total 2095.54334 As can be seen, the significance level was higher than 0.05, (2,32)= 22.156, which lead to the conclusion that there were significant differences between the groups.4.Findings Considering the data analysis, Experimental group 2 (exposed to accent reduction and text-to-speech software did better than the other groups.

There were no statistically significant differences in the pronunciation of single words (All the groups did equally well).However, there were statistically significant differences between the groups in the pronunciation of sentences (Experimental group 2 did significantly better than the other groups).During the semi-structured interview session, the participants in the experimental group 2 provided their opinions on accent reduction and text-to-speech software.

Addition of visual support Sheltered practice sessions in which the learner can take risks without stress and Immediate feedback Pronunciation of any word or sentence.Improving writing (They were probably talking about spelling).It is noteworthy to state that the integration of accent reduction and text-to-speech software into classrooms can help learners of English improve their pronunciation due to factors such as practice sessions in which the learner can take risks without stress and fear of error and immediate feedback.

6.

Limitations of the study and further research Since the study was carried out for 16 weeks and two hours for each week with a small number of participants due to the time constraint and the availability of the participants, it is suggested that similar experiments with a large number of subjects should be replicated.

References Baker, A.(1993).Tree or three?: An elementary pronunciation course.

Cambridge University Press.

Celce-Murcia, M., D.Brinton, & J.Goodwin.

(1996).Teaching Pronunciation.CUP DETYA .(2001).Teaching pronunciation: A handbook for teachers and trainers.Department of Education Training and Youth Affairs.Gonzlez, D.(2007).Text-to-speech applications used in EFL contexts to enhaTESL-EJ(2), 1-11.

Goodwin, J.

(2001).Teaching pronunciation.

In M.Celce-Murcia (Ed.) Teaching English as a second or foreign language (3rd ed.) (pp.117-138).

Boston: Heinle & Heinle.

Hansen, J.T.(2005).

Pronunciation teaching in the 21st century.

Review of Applied Linguistics in China: Issues in Language Learning and Teaching81-98.Jenkins, J.

(2005).Implementing an international approach to English pronunciation: The role of teacher attitudes and identity.

TESOL Quarterly, (3), 535-543.

Jenkins, J.(2004).Research in teaching pronunciation and intonation.Annual Review of Applied Linguistics, 24, 109-125.

kaya, F.(2006).

Text-to-speech technology: What does it offer to foreign language learners? CALL-EJ Online (2).

Levis, J.(2007).

Computer technology in teaching and researching pronunciation.Annual Review of Applied Linguistics, 184-202.

Ritchie, W.C., & Bhatia, T.K.(2008).Psycholinguistics.In B.Spolsky and F.M.Hult (eds), The Handbook of Educational Lingustics (pp.

38-52).Singapore: Blackwell publishing.lu, G.

(2005).Improving Students' Pronunciation Through Accent Reduction Software.

British Journal of Educational Technology(2), 303-316..
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