'chend' Met <e> – 'kind' Mit <e>: Using Big Data to Explore Phoneme-to-grapheme Mapping in Lucerne Swiss German
نویسندگان
چکیده
Speakers from the canton of Lucerne are infamous for spelling Middle High German (MHG) as when communicating in written Swiss German, e.g. Kind (‘child’) as . This phenomenon has been examined only impressionistically by phoneticians. This study provides a first account of this peculiarity of Lucerne Swiss German spellers: an analysis of normalised formant frequencies of two underlyingly MHG vowels from 200+ speakers of the Dialäkt Äpp corpus revealed that the Lucerne allophone is in reality [e] for most of the localities examined, which may explain why in vernacular writing, spellers prefer over . Homophony due to this peculiarity can cause misunderstandings in written and oral communication, and possibly has repercussions on the reading and writing development of Lucerne students. Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: http://doi.org/10.5167/uzh-129535 Published Version Originally published at: Zihlmann, Urban; Leemann, Adrian (2016). ‘Chend’ met – ‘Kind’ mit : using Big Data to explore phoneme-to-grapheme mapping in Lucerne Swiss German. In: 12. Tagung Phonetik und Phonologie im deutschsprachigen Raum, München, 12 October 2016 14 October 2016, 236-240. Urban Zihlmann, Adrian Leemann Phonetics Lab., Department of Theoretical and Applied Linguistics, University of Cambridge
منابع مشابه
Automatic speech recognition and translation of a Swiss German dialect: Walliserdeutsch
Walliserdeutsch is a Swiss German dialect spoken in the south west of Switzerland. To investigate the potential of automatic speech processing of Walliserdeutsch, a small database was collected based mainly on broadcast news from a local radio station. Experiments suggest that automatic speech recognition is feasible: use of another (Swiss German) database shows that the small data size lends i...
متن کاملPhoneme-to-grapheme mapping for spoken inquiries to the semantic web
Automatic methods for grapheme-to-phoneme (G2P) and phoneme-to-grapheme (P2G) conversion have become very popular in recent years. Their performance has improved considerably, while at the same time these developments required less input from expert lexicographers. Continuing in this tradition we will present in this paper a data-driven, language-independent approach called MASSIVE with which i...
متن کاملInvestigations on joint-multigram models for grapheme-to-phoneme conversion
We present a fully data-driven, language independent way of building a grapheme-to-phoneme converter. We apply the joint-multigram approach to the alignment problem and use standard language modelling techniques to model transcription probabilities. We study model parameters, training procedures and effects of corpus size in detail. Experiments were conducted on English and German pronunciation...
متن کاملSolving the Phoneme Conflict in Grapheme-to-Phoneme Conversion Using a Two-Stage Neural Network-Based Approach
To achieve high quality output speech synthesis systems, data-driven grapheme-to-phoneme (G2P) conversion is usually used to generate the phonetic transcription of out-of-vocabulary (OOV) words. To improve the performance of G2P conversion, this paper deals with the problem of conflicting phonemes, where an input grapheme can, in the same context, produce many possible output phonemes at the sa...
متن کاملComparison of Slovak and Czech speech recognition based on grapheme and phoneme acoustic models
Grapheme based mono-, crossand bilingual speech recognition of Czech and Slovak is presented in the paper. The training and testing procedures follow the MASPER initiative that was formed as a part of the COST 278 Action. All experiments were performed using Czech and Slovak SpeechDat-E databases. Grapheme-based models gave equivalent recognition performance compared to phoneme-based models in ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016