A High Quality Text-To-Speech System Composed of Multiple Neural Networks

While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing. We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images. The linguistic module employs a letter-to-sound neural network and a postlexical neural network. The acoustic module employs a duration neural network and a phonetic neural network. The visual neural network is employed in parallel to the acoustic module to drive a talking head. The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.Comment: Source link (9812006.tar.gz) contains: 1 PostScript file (4 pages) and 3 WAV audio files. If your system does not support Windows WAV files, try a tool like "sox" to translate the audio into a format of your choic

Dutch hypernym detection : does decompounding help?

This research presents experiments carried out to improve the precision and recall of Dutch hypernym detection. To do so, we applied a data-driven semantic relation finder that starts from a list of automatically extracted domain-specific terms from technical corpora, and generates a list of hypernym relations between these terms. As Dutch technical terms often consist of compounds written in one orthographic unit, we investigated the impact of a decompounding module on the performance of the hypernym detection system. In addition, we also improved the precision of the system by designing filters taking into account statistical and linguistic information. The experimental results show that both the precision and recall of the hypernym detection system improved, and that the decompounding module is especially effective for hypernym detection in Dutch

Pragmatic ability and disability as emergent phenomena

A holistic approach to pragmatic ability and disability is outlined which takes account both of the behaviour of individuals involved in the communicative process, and also of the underlying factors which contribute to such behaviour. Rather than being seen as resulting directly from a dysfunction in some kind of discrete pragmatic ‘module’ or behavioural mechanism, pragmatic impairment and also normal pragmatic functioning are instead viewed as the emergent consequence of interactions between linguistic, cognitive and sensorimotor processes which take place both within and between individuals

Basque linguistic atlas-ehha: from speech to automatic maps

The largely desired Basque linguistic atlas-EHHA is to be published nearly. The most important features of this atlas are shown in this paper. The EHHA is a linguistic project of the Academy of the Basque Language-Euskaltzaindia. We present the structure and the most important features of the data base and its different modules (module for the computerisation of data, module of lemmatisation and module of cartography). The module of lemmatisation is the necessary step to link the answers in phonetic characters and the linguistic maps based in orthographic ones. In this module the researcher must group answers, according to etymon, phonetic or morphological features. Furthermore, the maps of EHHA are based on Thiesen polygonation: each point has a polygon, in which the research puts the linguistic features using a colour's palette. As well as being coloured, the largest linguistic areas have also labels to make the interpretation of the map easier

Representing Translations on the Semantic Web

The increase of ontologies and data sets published in the Web in languages other than English raises some issues related to the representation of linguistic (multilingual) information in ontologies. Such linguistic descriptions can contribute to the establishment of links between ontologies and data sets described in multiple natural languages in the Linked Open Data cloud. For these reasons, several models have been proposed recently to enable richer linguistic descriptions in ontologies. Among them, we nd lemon, an RDF ontology-lexicon model that denes specic modules for dierent types of linguistic descriptions. In this contribution we propose a new module to represent translation relations between lexicons in dierent natural languages associated to the same ontology or belonging to dierent ontologies. This module can enable the representation of dierent types of translation relations, as well as translation metadata such as provenance or the reliability score of translations

Generation of multi-modal dialogue for a net environment

In this paper an architecture and special purpose markup language for simulated affective face-to-face communication is presented. In systems based on this architecture, users will be able to watch embodied conversational agents interact with each other in virtual locations on the internet. The markup language, or Rich Representation Language (RRL), has been designed to provide an integrated representation of speech, gesture, posture and facial animation

QuesNet: A Unified Representation for Heterogeneous Test Questions

Understanding learning materials (e.g. test questions) is a crucial issue in online learning systems, which can promote many applications in education domain. Unfortunately, many supervised approaches suffer from the problem of scarce human labeled data, whereas abundant unlabeled resources are highly underutilized. To alleviate this problem, an effective solution is to use pre-trained representations for question understanding. However, existing pre-training methods in NLP area are infeasible to learn test question representations due to several domain-specific characteristics in education. First, questions usually comprise of heterogeneous data including content text, images and side information. Second, there exists both basic linguistic information as well as domain logic and knowledge. To this end, in this paper, we propose a novel pre-training method, namely QuesNet, for comprehensively learning question representations. Specifically, we first design a unified framework to aggregate question information with its heterogeneous inputs into a comprehensive vector. Then we propose a two-level hierarchical pre-training algorithm to learn better understanding of test questions in an unsupervised way. Here, a novel holed language model objective is developed to extract low-level linguistic features, and a domain-oriented objective is proposed to learn high-level logic and knowledge. Moreover, we show that QuesNet has good capability of being fine-tuned in many question-based tasks. We conduct extensive experiments on large-scale real-world question data, where the experimental results clearly demonstrate the effectiveness of QuesNet for question understanding as well as its superior applicability