This paper presents an approach for integrating statistical ma-chine translation and automatic speech recognition for machine aided human translation (MAHT). It is applied to the problem of improving ASR performance for a human translator dictating translations in a target language while reading from a source language document. The approach addresses the issues asso-ciated with task independent ASR including out of vocabulary words and mismatched language models. We show in this paper that by obtaining domain information from the document in the form of labelled named entities from the source language text the accuracy of the ASR system can be improved by 34.5%. 1
In spoken language translation, integration of the ASR and MT components is critical for good perfor...
In the spoken language translation pipeline, machine translation systems that are trained solely on ...
Speech-to-speech translation is a challenging task mixing two of the most ambitious Natural Language...
This paper is concerned with the problem of machine aided human language translation. It addresses a...
Abstract—This study investigates the use of machine translated text for ASR domain adaptation. The p...
This paper is about Translation Dictation with ASR, that is, the use of Automatic Speech Recognitio...
In this paper, we study the incorporation of statistical machine translation models to automatic spe...
<p>We propose a novel technique for adapting text-based statistical machine translation to deal with...
For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models th...
Translating documents into multiple languages represents an extremely large expensefor businesses, g...
Speech translation is conventionally car-ried out by cascading an automatic speech recognition (ASR)...
This study investigates the possibility of using statistical machine translation to create domain-sp...
This paper describes an ongoing project which has the goal of improving machine translation quality ...
Spoken language translation (SLT) exists within one of the most challenging intersections of speech ...
We report insights from translating Spanish conversational telephone speech into English text by cas...
In spoken language translation, integration of the ASR and MT components is critical for good perfor...
In the spoken language translation pipeline, machine translation systems that are trained solely on ...
Speech-to-speech translation is a challenging task mixing two of the most ambitious Natural Language...
This paper is concerned with the problem of machine aided human language translation. It addresses a...
Abstract—This study investigates the use of machine translated text for ASR domain adaptation. The p...
This paper is about Translation Dictation with ASR, that is, the use of Automatic Speech Recognitio...
In this paper, we study the incorporation of statistical machine translation models to automatic spe...
<p>We propose a novel technique for adapting text-based statistical machine translation to deal with...
For automatic speech translation (AST), end-to-end approaches are outperformed by cascaded models th...
Translating documents into multiple languages represents an extremely large expensefor businesses, g...
Speech translation is conventionally car-ried out by cascading an automatic speech recognition (ASR)...
This study investigates the possibility of using statistical machine translation to create domain-sp...
This paper describes an ongoing project which has the goal of improving machine translation quality ...
Spoken language translation (SLT) exists within one of the most challenging intersections of speech ...
We report insights from translating Spanish conversational telephone speech into English text by cas...
In spoken language translation, integration of the ASR and MT components is critical for good perfor...
In the spoken language translation pipeline, machine translation systems that are trained solely on ...
Speech-to-speech translation is a challenging task mixing two of the most ambitious Natural Language...