Phonetic and Phonological Vowel Reduction in Russian

Jaworski, Sylwester

doi:10.2478/v10010-010-0003-9

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Vowel reduction, that is, a process that neutralizes phonological contrasts between vowels in unstressed syllables, is an essential linguistic phenomenon since vowels are the main syllabic element. Vowel reduction is one of the most characteristic features of stress-timed languages: in English, many vowels in unaccented syllables are reduced to schwa, whereas in Russian, the process appears to be more complex ( Jaworski, 2010 ). It is worth noting that the reduction of unstressed vowels is not displayed in Russian orthography.…”

Section: Introductionmentioning

confidence: 99%

Frequency Effects on Spelling Error Recognition: An ERP Study

Larionova

Martynova

2022

Front. Psychol.

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Spelling errors are ubiquitous in all writing systems. Most studies exploring spelling errors focused on the phonological plausibility of errors. However, unlike typical pseudohomophones, spelling errors occur in naturally produced written language. We investigated the time course of recognition of the most frequent orthographic errors in Russian (error in an unstressed vowel in the root) and the effect of word frequency on this process. During event-related potentials (ERP) recording, 26 native Russian speakers silently read high-frequency correctly spelled words, low-frequency correctly spelled words, high-frequency words with errors, and low-frequency words with errors. The amplitude of P200 was more positive for correctly spelled words than for misspelled words and did not depend on the frequency of the words. In addition, in the 350–500-ms time window, we found a more negative response for misspelled words than for correctly spelled words in parietal–temporal-occipital regions regardless of word frequency. Considering our results in the context of a dual-route model, we concluded that recognizing misspelled high-frequency and low-frequency words involves common orthographic and phonological processes associated with P200 and N400 components such as whole word orthography processing and activation of phonological representations correspondingly. However, at the 500–700 ms stage (associated with lexical-semantic access in our study), error recognition depends on the word frequency. One possible explanation for these differences could be that at the 500–700 ms stage recognition of high-frequency misspelled and correctly spelled words shifts from phonological to orthographic processes, while low-frequency misspelled words are accompanied by more prolonged phonological activation. We believe these processes may be associated with different ERP components P300 and N400, reflecting a temporal overlap between categorization processes based on orthographic properties for high-frequency words and phonological processes for low-frequency words. Therefore, our results complement existing reading models and demonstrate that the neuronal underpinnings of spelling error recognition during reading may depend on word frequency.

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Section: Introductionmentioning

confidence: 99%

Frequency Effects on Spelling Error Recognition: An ERP Study

Larionova

Martynova

2022

Front. Psychol.

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“…However, as anticipated, not all the target vowels were produced and the numbers were also not constant, especially as only vowels in lexical words in stressed syllables that were not preceded or followed by approximants and nasals were used. This was done as these sounds are likely to have co-articulatory effects on the vowels (see Deterding 1997;Jaworski 2010).…”

Section: Methodsmentioning

confidence: 99%

The production of English monophthong vowels by Iranian EFL learners

Pillai

Delavari

2012

Poznań Studies in Contemporary Linguistics

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This paper examines the production of English monophthong vowels by a group of Iranian learners of English based on an instrumental analysis of the vowels. Data in two speaking contexts were elicited from a group of learners between the ages of 16 and 17.To examine vowel quality, the first and second formant frequencies of the vowels were measured. These values and the duration of the vowels were compared to investigate the extent to which vowel contrast was maintained. The findings suggest that the learners tend not to contrast vowels (in terms of quality) when there is only one category of a similar vowel in Persian (e.g. English /iː/-/ɪ/ and /ʊ/-/uː/) whereas quality rather than length contrast is maintained for /e/-/ae/, both of which occur in Persian.

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“…Mireguli et al [36] proposed an algorithm to identify the Uyghur vowel weakening based on the word and syllable structure. Other languages have similar situations [37][38][39][40][41][42][43][44]. Uyghur vowel weakening occurs frequently in written form.…”

Section: Vowel Weakeningmentioning

confidence: 99%

Research on Uyghur Pattern Matching Based on Syllable Features

Abliz

Maimaiti

Wu³

et al. 2020

Information

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Pattern matching is widely used in various fields such as information retrieval, natural language processing (NLP), data mining and network security. In Uyghur (a typical agglutinative, low-resource language with complex morphology, spoken by the ethnic Uyghur group in Xinjiang, China), research on pattern matching is also ongoing. Due to the language characteristics, the pattern matching using characters and words as basic units has insufficient performance. There are two problems for pattern matching: (1) vowel weakening and (2) morphological changes caused by suffixes. In view of the above problems, this paper proposes a Boyer–Moore-U (BM-U) algorithm and a retrievable syllable coding format based on the syllable features of the Uyghur language and the improvement of the Boyer–Moore (BM) algorithm. This algorithm uses syllable features to perform pattern matching, which effectively solves the problem of weakening vowels, and it can better match words with stem shape changes. Finally, in the pattern matching experiments based on character-encoded text and syllable-encoded text for vowel-weakened words, the BM-U algorithm precision, recall, F1-measure and accuracy are improved by 4%, 55%, 33%, 25% and 10%, 52%, 38%, 38% compared to the BM algorithm.

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Phonetic and Phonological Vowel Reduction in Russian

Cited by 7 publications

References 4 publications

Frequency Effects on Spelling Error Recognition: An ERP Study

Frequency Effects on Spelling Error Recognition: An ERP Study

The production of English monophthong vowels by Iranian EFL learners

Research on Uyghur Pattern Matching Based on Syllable Features

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