Carlos-D. Martínez-Hinarejos

Also published as: Carlos D. Martínez, Carlos D. Martínez Hinarejos, Carlos D. Martínez-Hinarejos


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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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Evaluation of HMM-based Models for the Annotation of Unsegmented Dialogue Turns
Carlos-D. Martínez-Hinarejos | Vicent Tamarit | José-M. Benedí
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Corpus-based dialogue systems rely on statistical models, whose parameters are inferred from annotated dialogues. The dialogues are usually annotated in terms of Dialogue Acts (DA), and the manual annotation is difficult (as annotation rule are hard to define), error-prone and time-consuming. Therefore, several semi-automatic annotation processes have been proposed to speed-up the process and consequently obtain a dialogue system in less total time. These processes are usually based on statistical models. The standard statistical annotation model is based on Hidden Markov Models (HMM). In this work, we explore the impact of different types of HMM, with different number of states, on annotation accuracy. We performed experiments using these models on two dialogue corpora (Dihana and SwitchBoard) of dissimilar features. The results show that some types of models improve standard HMM in a human-computer task-oriented dialogue corpus (Dihana corpus), but their impact is lower in a human-human non-task-oriented dialogue corpus (SwitchBoard corpus).


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Improving Unsegmented Statistical Dialogue Act Labelling
Vicent Tamarit | Carlos-D. Martínez-Hinarejos | José Miguel Benedí Ruíz
Proceedings of the International Conference RANLP-2009

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A Study of a Segmentation Technique for Dialogue Act Assignation (short paper)
Carlos-D. Martínez-Hinarejos
Proceedings of the Eight International Conference on Computational Semantics

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Simultaneous Dialogue Act Segmentation and Labelling using Lexical and Syntactic Features
Ramon Granell | Stephen Pulman | Carlos-D. Martínez-Hinarejos
Proceedings of the SIGDIAL 2009 Conference

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Improving Unsegmented Dialogue Turns Annotation with N-gram Transducers
Carlos-D. Martínez-Hinarejos | Vicent Tamarit | José-Miguel Benedí
Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation, Volume 1


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Evaluation of several Maximum Likelihood Linear Regression Variants for Language Adaptation
Míriam Luján | Carlos D. Martínez | Vicent Alabau
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

Multilingual Automatic Speech Recognition (ASR) systems are of great interest in multilingual environments. We studied the case of the Comunitat Valenciana where the two official languages are Spanish and Valencian. These two languages share most of their phonemes, and their syntax and vocabulary are also quite similar since they have influenced each other for many years. We constructed a system, and trained its acoustic models with a small corpus of Spanish and Valencian, which has produced poor results due to the lack of data. Adaptation techniques can be used to adapt acoustic models that are trained with a large corpus of a language inr order to obtain acoustic models for a phonetically similar language. This process is known as language adaptation. The Maximum Likelihood Linear Regression (MLLR) technique has commonly been used in speaker adaptation; however we have used MLLR in language adaptation. We compared several MLLR variants (mean square, diagonal matrix and full matrix) for language adaptation in order to choose the best alternative for our system.

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Evaluation of Different Segmentation Techniques for Dialogue Turns
Carlos D. Martínez-Hinarejos | Vicent Tamarit
Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)

In dialogue systems, it is necessary to decode the user input into semantically meaningful units. These semantical units, usually Dialogue Acts (DA), are used by the system to produce the most appropriate response. The user turns can be segmented into utterances, which are meaningful segments from the dialogue viewpoint. In this case, a single DA is associated to each utterance. Many previous works have used DA assignation models on segmented dialogue corpora, but only a few have tried to perform the segmentation and assignation at the same time. The knowledge of the segmentation of turns into utterances is not common in dialogue corpora, and knowing the quality of the segmentations provided by the models that simultaneously perform segmentation and assignation would be interesting. In this work, we evaluate the accuracy of the segmentation offered by this type of model. The evaluation is done on a Spanish dialogue system on a railway information task. The results reveal that one of these techniques provides a high quality segmentation for this corpus.


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On the Training Data Requirements for an Automatic Dialogue Annotation Technique
Carlos D. Martínez-Hinarejos
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue


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Segmented and Unsegmented Dialogue-Act Annotation with Statistical Dialogue Models
Carlos D. Martínez Hinarejos | Ramón Granell | José Miguel Benedí
Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions

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Bilingual speech corpus in two phonetically similar languages
Vicente Alabau | Carlos D. Martínez
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

As Speech Recognition Systems improve, they become suitable for facingnew problems. Multilingual speech recognition is one such problems.In the present work, the case of the Comunitat Valenciana multilingual environment is studied.The official languages in the Comunitat Valenciana (Spanish and Valencian) share most of their acoustic units, and their vocabularies and syntax are quite similar.They have influenced each other for many years.A small corpus on an Information System task was developed for experimentationpurposes.This choice will make it possible to develop a working prototype in the future,and it is simple enough to build semi-automatic language models.The design of the acoustic corpus is discussed, showing that all combinations of accents have been studied (native, non-native speakers, male, female, etc.).


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A Labelling Proposal to Annotate Dialogues
Carlos D. Martínez-Hinarejos | Emilio Sanchís | Fernando García-Granada | Pablo Aibar
Proceedings of the Third International Conference on Language Resources and Evaluation (LREC’02)