23 research outputs found
Translation dictation vs. post-editing with cloud-based voice recognition: a pilot experiment
In this paper, we report on a pilot mixed-methods experiment investigating the effects on
productivity and on the translator experience of integrating machine translation (MT) postediting (PE) with voice recognition (VR) and translation dictation (TD). The experiment
was performed with a sample of native Spanish participants. In the quantitative phase of the
experiment, they performed four tasks under four different conditions, namely (1)
conventional TD; (2) PE in dictation mode; (3) TD with VR; and (4) PE with VR (PEVR).
In the follow-on qualitative phase, the participants filled out an online survey, providing
details of their perceptions of the task and of PEVR in general. Our results suggest that
PEVR may be a usable way to add MT to a translation workflow, with some caveats. When
asked about their experience with the tasks, our participants preferred translation without the
‘constraint’ of MT, though the quantitative results show that PE tasks were generally more
efficient. This paper provides a brief overview of past work exploring VR for from-scratch
translation and PE purposes, describes our pilot experiment in detail, presents an overview
and analysis of the data collected, and outlines avenues for future work
Speech Recognition
Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes
Artificial Intelligence Index Report 2023 - HAI
Le ‘Human-Centered Artificial Intelligence (HAI, Stanford University) annonce la publication de son rapport annuel « Artificial Intelligence Index Report 2023″ qui rassemble et visualise les tendances relatives à l’intelligence artificielle