Jorge Civera Saiz


2022

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MLLP-VRAIN UPV systems for the IWSLT 2022 Simultaneous Speech Translation and Speech-to-Speech Translation tasks
Javier Iranzo-Sánchez | Javier Jorge Cano | Alejandro Pérez-González-de-Martos | Adrián Giménez Pastor | Gonçal Garcés Díaz-Munío | Pau Baquero-Arnal | Joan Albert Silvestre-Cerdà | Jorge Civera Saiz | Albert Sanchis | Alfons Juan
Proceedings of the 19th International Conference on Spoken Language Translation (IWSLT 2022)

This work describes the participation of the MLLP-VRAIN research group in the two shared tasks of the IWSLT 2022 conference: Simultaneous Speech Translation and Speech-to-Speech Translation. We present our streaming-ready ASR, MT and TTS systems for Speech Translation and Synthesis from English into German. Our submission combines these systems by means of a cascade approach paying special attention to data preparation and decoding for streaming inference.

2021

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Stream-level Latency Evaluation for Simultaneous Machine Translation
Javier Iranzo-Sánchez | Jorge Civera Saiz | Alfons Juan
Findings of the Association for Computational Linguistics: EMNLP 2021

Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation, resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.

2020

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Direct Segmentation Models for Streaming Speech Translation
Javier Iranzo-Sánchez | Adrià Giménez Pastor | Joan Albert Silvestre-Cerdà | Pau Baquero-Arnal | Jorge Civera Saiz | Alfons Juan
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. These systems are usually connected by a segmenter that splits the ASR output into hopefully, semantically self-contained chunks to be fed into the MT system. This is specially challenging in the case of streaming ST, where latency requirements must also be taken into account. This work proposes novel segmentation models for streaming ST that incorporate not only textual, but also acoustic information to decide when the ASR output is split into a chunk. An extensive and throughly experimental setup is carried out on the Europarl-ST dataset to prove the contribution of acoustic information to the performance of the segmentation model in terms of BLEU score in a streaming ST scenario. Finally, comparative results with previous work also show the superiority of the segmentation models proposed in this work.

2015

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The MLLP ASR systems for IWSLT 2015
Miguel Ángel Del Agua Teba | Adrià Agusti Martinez Villaronga | Santiago Piqueras Gozalbes | Adrià Giménez Pastor | José Alberto Sanchis Navarro | Jorge Civera Saiz | Alfons Juan-Císcar
Proceedings of the 12th International Workshop on Spoken Language Translation: Evaluation Campaign