Proceedings ArticleDOI
Comparison Of Grapheme-to-Phoneme Conversions For Spoken Document Retrieval
Dmitriy Prozorov,Alexandra Tatarinova +1 more
- pp 1-4
TLDR
Analysis of spoken document retrieval techniques which apply word similarity based on phonemic transcriptions building or approximate string matching on the collection of spoken documents with speech on Russian language is obtained.Abstract:
The article contains analysis of spoken document retrieval techniques which apply word similarity based on phonemic transcriptions building or approximate string matching. Results are obtained on the collection of spoken documents with speech on Russian language. Grapheme-to-phoneme conversion methods based on a hidden Markov model and 1,2-order finite Markov chain is discussed on the article.read more
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References
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Journal ArticleDOI
Joint-sequence models for grapheme-to-phoneme conversion
Maximilian Bisani,Hermann Ney +1 more
TL;DR: A novel estimation algorithm is presented that demonstrates high accuracy on a variety of databases and studies the impact of the maximum approximation in training and transcription, the interaction of model size parameters, n-best list generation, confidence measures, and phoneme-to-grapheme conversion.
Proceedings Article
WFST-Based Grapheme-to-Phoneme Conversion: Open Source tools for Alignment, Model-Building and Decoding
TL;DR: This paper introduces a new open source, WFST-based toolkit for Grapheme-toPhoneme conversion that is efficient, accurate and currently supports a range of features including EM sequence alignment and several decoding techniques novel in the context of G2P.
Proceedings ArticleDOI
Grapheme-to-phoneme conversion based on high-order Markov chain for spoken term detection by text query
TL;DR: The paper presents a new grapheme-to-phoneme conversion method based on high-order Markov chain that is applied to retrieve of spoken documents in Russian language.
Proceedings ArticleDOI
Building Test Speech Dataset on Russian Language for Spoken Document Retrieval Task
TL;DR: A technique of creation of speech dataset is presented which is applied for test of spoken document retrieval methods and contains expert's indication of documents which are relevant to queries.