Luis Javier Rodríguez

Computer Assisted Transcription of Handwritten Text Images

To date, automatic handwriting recognition systems are far from being perfect and often they need a post editing where a human intervention is required to check and correct the results of such systems. We propose to have a new interactive, on-line framework which, rather than full automation, aims at assisting the human in the proper recognition- transcription process; that is, facilitate and speed up their transcription task of handwritten texts. This framework combines the efficiency of automatic handwriting recognition systems with the accuracy of the human transcriptor. The best result is a cost-effective perfect transcription of the handwriting text images.

Computer-assisted translation using speech recognition

Current machine translation systems are far from being perfect. However, such systems can be used in computer-assisted translation to increase the productivity of the (human) translation process. The idea is to use a text-to-text translation system to produce portions of target language text that can be accepted or amended by a human translator using text or speech. These user-validated portions are then used by the text-to-text translation system to produce further, hopefully improved suggestions. There are different alternatives of using speech in a computer-assisted translation system: From pure dictated translation to simple determination of acceptable partial translations by reading parts of the suggestions made by the system. In all the cases, information from the text to be translated can be used to constrain the speech decoding search space. While pure dictation seems to be among the most attractive settings, unfortunately perfect speech decoding does not seem possible with the current speech processing technology and human error-correcting would still be required. Therefore, approaches that allow for higher speech recognition accuracy by using increasingly constrained models in the speech recognition process are explored here. All these approaches are presented under the statistical framework. Empirical results support the potential usefulness of using speech within the computer-assisted translation paradigm.

Computer assisted transcription for ancient text images

Paleography experts spend many hours transcribing ancient documents and state-of-the-art handwritten text recognition systems are not suitable for performing this task automatically. We propose here a new interactive, on-line framework which, rather than full automation, aims at assisting the experts in the proper recognition-transcription process; that is, facilitate and speed up the transcription of old documents. This framework combines the efficiency of automatic handwriting recognition systems with the accuracy of the experts, leading to a cost-effective perfect transcription of ancient manuscripts.

Computer Assisted Transcription of Speech

Speech recognition systems have proved their usefulness in very different tasks. Nevertheless, the present state-of-the-art of the speech technologies does not make it possible to achieve perfect transcriptions in most of the cases. Owing to this fact, human intervention is necessary to check and correct the results of such systems. We present a novel approach that faces this problem by combining the efficiency of the automatic speech recognition systems with the accuracy of the human transcriptor. The result of this process is a cost-effective perfect transcription of the input signal.

On the application of different evolutionary algorithms to the alignment problem in statistical machine translation

In statistical machine translation, an alignment defines a mapping between the words in the source and in the target sentence. Alignments are used, on the one hand, to train the statistical models and, on the other, during the decoding process to link the words in the source sentence to the words in the partial hypotheses generated. In both cases, the quality of the alignments is crucial for the success of the translation process. In this paper, we propose several evolutionary algorithms for computing alignments between two sentences in a parallel corpus. This algorithm has been tested on different tasks involving different pair of languages. Specifically, in the two shared tasks proposed in the HLT-NAACL 2003 and in the ACL 2005, the EDA-based algorithm outperforms the best participant systems. In addition, the experiments show that, because of the limitations of the well known statistical alignment models, new improvements in alignments quality could not be achieved by using improved search algorithms only.

Searching for alignments in SMT. A novel approach based on an Estimation of Distribution Algorithm

In statistical machine translation, an alignment defines a mapping between the words in the source and in the target sentence. Alignments are used, on the one hand, to train the statistical models and, on the other, during the decoding process to link the words in the source sentence to the words in the partial hypotheses generated. In both cases, the quality of the alignments is crucial for the success of the translation process. In this paper, we propose an algorithm based on an Estimation of Distribution Algorithm for computing alignments between two sentences in a parallel corpus. This algorithm has been tested on different tasks involving different pair of languages. In the different experiments presented here for the two word-alignment shared tasks proposed in the HLT-NAACL 2003 and in the ACL 2005, the EDA-based algorithm outperforms the best participant systems.