Bayesian joint-sequence models for grapheme-to-phoneme conversion

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F17%3APU126426" target="_blank" >RIV/00216305:26230/17:PU126426 - isvavai.cz</a>

Result on the web

<a href="https://www.fit.vut.cz/research/publication/11469/" target="_blank" >https://www.fit.vut.cz/research/publication/11469/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2017.7952674" target="_blank" >10.1109/ICASSP.2017.7952674</a>

Result language

angličtina

Original language name

Bayesian joint-sequence models for grapheme-to-phoneme conversion

Original language description

We describe a fully Bayesian approach to grapheme-to-phoneme conversion based on the joint-sequence model (JSM). Usually, standard smoothed n-gram language models (LM, e.g. Kneser-Ney) are used with JSMs to model graphone sequences (joint graphemephoneme pairs). However, we take a Bayesian approach using a hierarchical Pitman-Yor-Process LM. This provides an elegant alternative to using smoothing techniques to avoid over-training. No held-out sets and complex parameter tuning is necessary, and several convergence problems encountered in the discounted Expectation- Maximization (as used in the smoothed JSMs) are avoided. Every step is modeled by weighted finite state transducers and implemented with standard operations from the OpenFST toolkit. We evaluate our model on a standard data set (CMUdict), where it gives comparable results to the previously reported smoothed JSMs in terms of phoneme-error rate while requiring a much smaller training/ testing time. Most importantly, our model can be used in a Bayesian framework and for (partly) un-supervised training.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

N - Vyzkumna aktivita podporovana z neverejnych zdroju

Publication year

2017

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of ICASSP 2017

ISBN

978-1-5090-4117-6

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

2836-2840

Publisher name

IEEE Signal Processing Society

Place of publication

New Orleans

Event location

New Orleans, USA

Event date

Mar 5, 2017

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000414286203002