Verónica López-Ludeña

Automatic categorization for improving Spanish into Spanish Sign Language machine translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Drivers License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82.

LSESpeak: A spoken language generator for Deaf people

This paper describes the development of LSESpeak, a spoken Spanish generator for Deaf people. This system integrates two main tools: a sign language into speech translation system and an SMS (Short Message Service) into speech translation system. The first tool is made up of three modules: an advanced visual interface (where a deaf person can specify a sequence of signs), a language translator (for generating the sequence of words in Spanish), and finally, an emotional text to speech (TTS) converter to generate spoken Spanish. The visual interface allows a sign sequence to be defined using several utilities. The emotional TTS converter is based on Hidden Semi-Markov Models (HSMMs) permitting voice gender, type of emotion, and emotional strength to be controlled. The second tool is made up of an SMS message editor, a language translator and the same emotional text to speech converter. Both translation tools use a phrase-based translation strategy where translation and target language models are trained from parallel corpora. In the experiments carried out to evaluate the translation performance, the sign language-speech translation system reported a 96.45 BLEU and the SMS-speech system a 44.36 BLEU in a specific domain: the renewal of the Identity Document and Driving License. In the evaluation of the emotional TTS, it is important to highlight the improvement in the naturalness thanks to the morpho-syntactic features, and the high flexibility provided by HSMMs when generating different emotional strengths.

Increasing adaptability of a speech into sign language translation system

This paper describes a new version of a speech into sign language translation system with new tools and characteristics for increasing its adaptability to a new task or a new semantic domain. This system is made up of a speech recognizer (for decoding the spoken utterance into a word sequence), a natural language translator (for converting a word sequence into a sequence of signs belonging to the sign language), and a 3D avatar animation module (for playing back the signs). In order to increase the system adaptability, this paper presents new improvements in all the three main modules for generating automatically the task dependent information from a parallel corpus: automatic generation of Spanish variants when generating the vocabulary and language model for the speech recogniser, an acoustic adaptation module for the speech recogniser, data-oriented language and translation models for the machine translator and a list of signs to design. The avatar animation module includes a new editor for rapidly design of the required signs. These developments have been necessary to reduce the effort when adapting a Spanish into Spanish sign language (LSE: Lengua de Signos Espanola) translation system to a new domain. The whole translation presents a SER (Sign Error Rate) lower than 10% and a BLEU higher than 90% while the effort for adapting the system to a new domain has been reduced more than 50%.

I feel you: towards affect-sensitive domotic spoken conversational agents

We describe the work on infusion of emotion into limited-task autonomous spoken conversational agents (SCAs) situated in the domestic environment, using a N eed-inspired task-independent Emo tion model (NEMO). In order to demonstrate the generation of affect through the use of the model, we describe the work of integrating it with a natural-language mixed-initiative HiFi-control SCA. NEMO and the host system communicates externally, removing the need for the Dialog Manager to be modified as done in most existing dialog systems, in order to be adaptive. We also summarize the work on automatic affect prediction, namely frustration and contentment from dialog features, a non-conventional source, in the attempt of moving towards a more user-centric approach.