Work

Multilingual number transcription for text-to-speech conversion.

Year: 2013

Type: conference-paper

Abstract: This paper describes the text normalization module of a text to speech fully-trainable conversion system and its application to number transcription. The main target is to generate a language independent text normalization module, based on data instead of on expert rules. This paper proposes a general architecture based on statistical machine translation techniques. This proposal is composed of three main modules: a tokenizer for splitting the text input into a token graph, a phrase-based translation module for token translation, and a post-processing module for removing some tokens. This architecture has been evaluated for number transcription in several languages: English, Spanish and Romanian. Number transcription is an important aspect in the text normalization problem.

Source: UPM Digital Archive (Technical University of Madrid)

Authors: Rubén San-Segundo-Hernández, Juan Manuel Montero, Mircea Giurgiu, Ioana Muresan, Simon King

Institution: Universidad Politécnica de Madrid

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: European Commission, Ministerio de Ciencia e Innovación

Awards: G1431523498, G2220330510, Speech synthesis that improves through adaptive learning

OpenAlex ID: W18915075