Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription
Orthographic-To-Phonetic (O2P) Transcription is the process of learning the relationship between the written word and its phonetic transcription. It is a necessary part of Text-To-Speech (TTS) systems and it plays an important role in handling Out-Of-Vocabulary (OOV) words in Automatic Speech Recogn...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Institute of Fundamental Technological Research Polish Academy of Sciences
2021-06-01
|
| Series: | Archives of Acoustics |
| Subjects: | |
| Online Access: | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2788 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849693317920456704 |
|---|---|
| author | El-Hadi CHERIFI Mhania GUERTI |
| author_facet | El-Hadi CHERIFI Mhania GUERTI |
| author_sort | El-Hadi CHERIFI |
| collection | DOAJ |
| description | Orthographic-To-Phonetic (O2P) Transcription is the process of learning the relationship between the written word and its phonetic transcription. It is a necessary part of Text-To-Speech (TTS) systems and it plays an important role in handling Out-Of-Vocabulary (OOV) words in Automatic Speech Recognition systems. The O2P is a complex task, because for many languages, the correspondence between the orthography and its phonetic transcription is not completely consistent. Over time, the techniques used to tackle this problem have evolved, from earlier rules based systems to the current more sophisticated machine learning approaches. In this paper, we propose an approach for Arabic O2P Conversion based on a probabilistic method: Conditional Random Fields (CRF). We discuss the results and experiments of this method apply on a pronunciation dictionary of the Most Commonly used Arabic Words, a database that we called (MCAW-Dic). MCAW-Dic contains over 35 000 words in Modern Standard Arabic (MSA) and their pronunciation, a database that we have developed by ourselves assisted by phoneticians and linguists from the University of Tlemcen. The results achieved are very satisfactory and point the way towards future innovations. Indeed, in all our tests, the score was between 11 and 15% error rate on the transcription of phonemes (Phoneme Error Rate). We could improve this result by including a large context, but in this case, we encountered memory limitations and calculation difficulties. |
| format | Article |
| id | doaj-art-ae7ced46a0f74accba2dd9bd59e1a9a4 |
| institution | DOAJ |
| issn | 0137-5075 2300-262X |
| language | English |
| publishDate | 2021-06-01 |
| publisher | Institute of Fundamental Technological Research Polish Academy of Sciences |
| record_format | Article |
| series | Archives of Acoustics |
| spelling | doaj-art-ae7ced46a0f74accba2dd9bd59e1a9a42025-08-20T03:20:27ZengInstitute of Fundamental Technological Research Polish Academy of SciencesArchives of Acoustics0137-50752300-262X2021-06-0146210.24425/aoa.2021.136574Conditional Random Fields Applied to Arabic Orthographic-Phonetic TranscriptionEl-Hadi CHERIFI0Mhania GUERTI1National Polytechnic SchoolNational Polytechnic SchoolOrthographic-To-Phonetic (O2P) Transcription is the process of learning the relationship between the written word and its phonetic transcription. It is a necessary part of Text-To-Speech (TTS) systems and it plays an important role in handling Out-Of-Vocabulary (OOV) words in Automatic Speech Recognition systems. The O2P is a complex task, because for many languages, the correspondence between the orthography and its phonetic transcription is not completely consistent. Over time, the techniques used to tackle this problem have evolved, from earlier rules based systems to the current more sophisticated machine learning approaches. In this paper, we propose an approach for Arabic O2P Conversion based on a probabilistic method: Conditional Random Fields (CRF). We discuss the results and experiments of this method apply on a pronunciation dictionary of the Most Commonly used Arabic Words, a database that we called (MCAW-Dic). MCAW-Dic contains over 35 000 words in Modern Standard Arabic (MSA) and their pronunciation, a database that we have developed by ourselves assisted by phoneticians and linguists from the University of Tlemcen. The results achieved are very satisfactory and point the way towards future innovations. Indeed, in all our tests, the score was between 11 and 15% error rate on the transcription of phonemes (Phoneme Error Rate). We could improve this result by including a large context, but in this case, we encountered memory limitations and calculation difficulties.https://acoustics.ippt.pan.pl/index.php/aa/article/view/2788Orthographic-To-Phonetic TranscriptionConditional Random Fieldstext-to-speechArabic speech synthesisModern Standard Arabic |
| spellingShingle | El-Hadi CHERIFI Mhania GUERTI Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription Archives of Acoustics Orthographic-To-Phonetic Transcription Conditional Random Fields text-to-speech Arabic speech synthesis Modern Standard Arabic |
| title | Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription |
| title_full | Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription |
| title_fullStr | Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription |
| title_full_unstemmed | Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription |
| title_short | Conditional Random Fields Applied to Arabic Orthographic-Phonetic Transcription |
| title_sort | conditional random fields applied to arabic orthographic phonetic transcription |
| topic | Orthographic-To-Phonetic Transcription Conditional Random Fields text-to-speech Arabic speech synthesis Modern Standard Arabic |
| url | https://acoustics.ippt.pan.pl/index.php/aa/article/view/2788 |
| work_keys_str_mv | AT elhadicherifi conditionalrandomfieldsappliedtoarabicorthographicphonetictranscription AT mhaniaguerti conditionalrandomfieldsappliedtoarabicorthographicphonetictranscription |