A statistical text-to-phone function using ngrams and rules

Authors:
W. M. Fisher
Affiliations:
Nat. Inst. of Stand. & Technol., Gaithersburg, MD, USA
Venue:
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Year:
1999

Citing 0
Cited 8

Lecture and Presentation Tracking in an Intelligent Meeting Room

ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
A System for Multiattribute Drug Product Comparison

Journal of Medical Systems
Learning Local Transductions Is Hard

Journal of Logic, Language and Information
Pronunciation modeling for improved spelling correction

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Specifics of Hidden Markov Model Modifications for Large Vocabulary Continuous Speech Recognition

Informatica
The ISL RT-07 Speech-to-Text System

Multimodal Technologies for Perception of Humans
Pronunciation modeling in spelling correction for writers of English as a foreign language

SRWS '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Student Research Workshop and Doctoral Consortium
The ISL RT-06S speech-to-text system

MLMI'06 Proceedings of the Third international conference on Machine Learning for Multimodal Interaction

Quantified Score

Hi-index	0.00

Visualization

Abstract

Adopting concepts from statistical language modeling and rule-based transformations can lead to effective and efficient text-to-phone (TTP) functions. We present here the methods and results of one such effort, resulting in a relatively compact and fast set of TTP rules that achieves 94.5% segmental phonemic accuracy.