Haritz Arzelus


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Impact of Automatic Segmentation on the Quality, Productivity and Self-reported Post-editing Effort of Intralingual Subtitles
Aitor Álvarez | Marina Balenciaga | Arantza del Pozo | Haritz Arzelus | Anna Matamala | Carlos-D. Martínez-Hinarejos
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

This paper describes the evaluation methodology followed to measure the impact of using a machine learning algorithm to automatically segment intralingual subtitles. The segmentation quality, productivity and self-reported post-editing effort achieved with such approach are shown to improve those obtained by the technique based in counting characters, mainly employed for automatic subtitle segmentation currently. The corpus used to train and test the proposed automated segmentation method is also described and shared with the community, in order to foster further research in this area.


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Phoneme Similarity Matrices to Improve Long Audio Alignment for Automatic Subtitling
Pablo Ruiz | Aitor Álvarez | Haritz Arzelus
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

Long audio alignment systems for Spanish and English are presented, within an automatic subtitling application. Language-specific phone decoders automatically recognize audio contents at phoneme level. At the same time, language-dependent grapheme-to-phoneme modules perform a transcription of the script for the audio. A dynamic programming algorithm (Hirschberg’s algorithm) finds matches between the phonemes automatically recognized by the phone decoder and the phonemes in the script’s transcription. Alignment accuracy is evaluated when scoring alignment operations with a baseline binary matrix, and when scoring alignment operations with several continuous-score matrices, based on phoneme similarity as assessed through comparing multivalued phonological features. Alignment accuracy results are reported at phoneme, word and subtitle level. Alignment accuracy when using the continuous scoring matrices based on phonological similarity was clearly higher than when using the baseline binary matrix.