https://hal.archives-ouvertes.fr/hal-00499203/document

Joint-sequence models for grapheme-to-phoneme conversion

The mixed decision tree includes a network of yes-no questions about adjacent letters in a spelled word sequence and also about adjacent phonemes in the phoneme sequence corresponding to the spelled word sequence. Leaf nodes of the mixed decision tree provide information about which phonetic transcriptions are most probable. Using the mixed trees, scores are developed for each of a plurality of possible pronunciations, and these scores can be used to select the best pronunciation as well as to rank pronunciations in order of probability. The pronunciations generated by the system can be used in speech synthesis and speech recognition applications as well as lexicography applications.

Method and apparatus using decision trees to generate and score multiple pronunciations for a spelled word

Although the Arab world has an estimated number of 250 million Arabic speakers, there has been little research on Arabic speech recognition when compared to other languages of similar importance (e.g. Mandarin). Due to the lack of diacritic Arabic text and the lack of Pronunciation Dictionary (PD), most of previous work on Arabic Automatic Speech Recognition has been concentrated on developing recognizers using Romanized characters i.e. let the system recognizes the Arabic word as an English one, then map it to Arabic word from lookup table that maps the Arabic word to its Romanized pronunciation.

Arabic speech recognition using SPHINX engine

Pronunciation prediction with Default&Refine

Machine transliteration is a method for automatically converting words in one language into phonetically equivalent ones in another language. Machine transliteration plays an important role in natural language applications such as information retrieval and machine translation, especially for handling proper nouns and technical terms. Four machine transliteration models - grapheme-based transliteration model, phoneme-based transliteration model, hybrid transliteration model, and correspondence-based transliteration model - have been proposed by several researchers. To date, however, there has been little research on a framework in which multiple transliteration models can operate simultaneously. Furthermore, there has been no comparison of the four models within the same framework and using the same data. We addressed these problems by 1) modeling the four models within the same framework, 2) comparing them under the same conditions, and 3) developing a way to improve machine transliteration through this comparison. Our comparison showed that the hybrid and correspondence-based models were the most effective and that the four models can be used in a complementary manner to improve machine transliteration performance.

/pdf/a-comparison-of-different-machine-transliteration-models-37sxydzk9i.pdf

A comparison of different machine transliteration models

Recently, we described a two step self learning approach for grapheme to phoneme (G2P) conversion (O. Anderson and P. Dalsgaard, 1995). In the first step, grapheme and phoneme strings in the training data are aligned via an iterative Viterbi procedure that may insert graphemic and phonemic nulls where required. In the second step, a Trie structure, encoding pronunciation rules is generated. We describe the alignment module, and give alignment accuracies on the NETtalk database. We also compare transcription accuracies for two approaches to the second step on three databases: the NETtalk database, the CMU dictionary and the French part of the ONOMASTICA lexicon. The two transcription approaches applied in this research are a Trie approach and an approach based on binary decision trees grown by means of the Gelfand-Ravishankar-Delp algorithm (F. Breiman et al., 1984; S. Gelfand et al., 1991; R. Kuhn et al., 1995). We discuss the choice of questions for these decision trees-it may be possible to formulate questions about groups of characters (e.g., "is the next letter a vowel?") that yield better trees than those that only use questions about individual characters (e.g., "is the next letter an 'A' ?"). Finally, we discuss the implications of our work for G2P conversion.

/pdf/comparison-of-two-tree-structured-approaches-for-grapheme-to-dvohbkyv1y.pdf

Comparison of two tree-structured approaches for grapheme-to-phoneme conversion

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

A. Lazarides

Author Tools

Chat about Author

Papers

Comparison of two tree-structured approaches for grapheme-to-phoneme conversion