This paper describes the development of and the first experiments in a Spanish to sign language translation system in a real domain. The developed system focuses on the sentences spoken by an official when assisting people applying for, or renewing their Identity Card. The system translates official explanations into Spanish Sign Language (LSE: Lengua de Signos Españ ola) for Deaf people. The translation 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 hand movements). Two proposals for natural language translation have been evaluated: a rule-based translation module (that computes sign confidence measures from the word confidence measures obtained in the speech recognition module) and a statistical translation module (in this case, parallel corpora were used for training the statistical model). The best configuration reported 31.6% SER (Sign Error Rate) and 0.5780 BLEU (BiLingual Evaluation Understudy). The paper also describes the eSIGN 3D avatar animation module (considering the sign confidence), and the limitations found when implementing a strategy for reducing the delay between the spoken utterance and the sign sequence animation.
It is well known that the emotional state of a speaker usually alters the way she/he speaks. Although all the components of the voice can be affected by emotion in some statistically-significant way, not all these deviations from a neutral voice are identified by human listeners as conveying emotional information.In this paper we have carried out several perceptual and objective experiments that show the relevance of prosody and segmental spectrum in the characterization and identification of four emotions in Spanish.A Bayes classifier has been used in the objective emotion identification task. Emotion models were generated as the contribution of every emotion to the buildup of a Universal Background Emotion Codebook.According to our experiments, surprise is primarily identified by humans through its prosodic rubric (in spite of some automatically-identifiable segmental characteristics); while for anger the situation is just the opposite. Sadness and happiness need a combination of prosodic and segmental rubrics to be reliably identified.
In this paper, we present several innovative techniques that can be applied in a PPRLM system for language identification (LID). We will show how we obtained a 535%0 relative error reduction from our base system using several techniques. First, the application of a variable threshold in score computation, dependent on the average scores in the language model, provided a 35% error reduction. A random selection of sentences for the different sets and the use of silence models also improved the system. Then, to improve the classifier, we compared the bias removal technique (up to 19% error reduction) and a Gaussian classifier (up to 37%0 error reduction). Finally, we included the acoustic score in the Gaussian classifier (2% error reduction) and increased the number of Gaussians to have a multipleGaussian classifier (14% error reduction). We will show how all these improvements are remarkable as they have been mostly additive.
In this paper a Bayesian Networks, BNs, approach to dialogue modelling [1] is evaluated in terms of a battery of both subjective and objective metrics. A significant effort in improving the contextual information handling capabilities of the system has been done. Consequently, besides typical dialogue measurement rates for usability like task or dialogue completion rates, dialogue time, etc. we have included a new figure measuring the contextuality of the dialogue as the number of turns where contextual information is helpful for dialogue resolution. The evaluation is developed through a set of predefined scenarios according to different initiative styles and focusing on the impact of the user's level of experience.
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