Marc Tessier


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Terminology in Neural Machine Translation: A Case Study of the Canadian Hansard
Rebecca Knowles | Samuel Larkin | Marc Tessier | Michel Simard
Proceedings of the 24th Annual Conference of the European Association for Machine Translation

Incorporating terminology into a neural machine translation (NMT) system is a feature of interest for many users of machine translation. In this case study of English-French Canadian Parliamentary text, we examine the performance of standard NMT systems at handling terminology and consider the tradeoffs between potential performance improvements and the efforts required to maintain terminological resources specifically for NMT.

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ReadAlong Studio Web Interface for Digital Interactive Storytelling
Aidan Pine | David Huggins-Daines | Eric Joanis | Patrick Littell | Marc Tessier | Delasie Torkornoo | Rebecca Knowles | Roland Kuhn | Delaney Lothian
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

We develop an interactive web-based user interface for performing textspeech alignment and creating digital interactive “read-along audio books that highlight words as they are spoken and allow users to replay individual words when clicked. We build on an existing Python library for zero-shot multilingual textspeech alignment (Littell et al., 2022), extend it by exposing its functionality through a RESTful API, and rewrite the underlying speech recognition engine to run in the browser. The ReadAlong Studio Web App is open-source, user-friendly, prioritizes privacy and data sovereignty, allows for a variety of standard export formats, and is designed to work for the majority of the world’s languages.


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ReadAlong Studio: Practical Zero-Shot Text-Speech Alignment for Indigenous Language Audiobooks
Patrick Littell | Eric Joanis | Aidan Pine | Marc Tessier | David Huggins Daines | Delasie Torkornoo
Proceedings of the 1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages

While the alignment of audio recordings and text (often termed “forced alignment”) is often treated as a solved problem, in practice the process of adapting an alignment system to a new, under-resourced language comes with significant challenges, requiring experience and expertise that many outside of the speech community lack. This puts otherwise “solvable” problems, like the alignment of Indigenous language audiobooks, out of reach for many real-world Indigenous language organizations. In this paper, we detail ReadAlong Studio, a suite of tools for creating and visualizing aligned audiobooks, including educational features like time-aligned highlighting, playing single words in isolation, and variable-speed playback. It is intended to be accessible to creators without an extensive background in speech or NLP, by automating or making optional many of the specialist steps in an alignment pipeline. It is well documented at a beginner-technologist level, has already been adapted to 30 languages, and can work out-of-the-box on many more languages without adaptation.