1,088 research outputs found
Development of a speech recognition system for Spanish broadcast news
This paper reports on the development process of a speech recognition system for Spanish broadcast news within the MESH FP6 project. The system uses the SONIC recognizer developed at the Center for Spoken Language Research (CSLR), University of Colorado. Acoustic and language models were trained using Hub4 broadcast news data. Experiments and evaluation results are reported
From Word to Sense Embeddings: A Survey on Vector Representations of Meaning
Over the past years, distributed semantic representations have proved to be
effective and flexible keepers of prior knowledge to be integrated into
downstream applications. This survey focuses on the representation of meaning.
We start from the theoretical background behind word vector space models and
highlight one of their major limitations: the meaning conflation deficiency,
which arises from representing a word with all its possible meanings as a
single vector. Then, we explain how this deficiency can be addressed through a
transition from the word level to the more fine-grained level of word senses
(in its broader acceptation) as a method for modelling unambiguous lexical
meaning. We present a comprehensive overview of the wide range of techniques in
the two main branches of sense representation, i.e., unsupervised and
knowledge-based. Finally, this survey covers the main evaluation procedures and
applications for this type of representation, and provides an analysis of four
of its important aspects: interpretability, sense granularity, adaptability to
different domains and compositionality.Comment: 46 pages, 8 figures. Published in Journal of Artificial Intelligence
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Semantic Topic Models: Combining Word Distributional Statistics and Dictionary Definitions
In this paper, we propose a novel topic model based on incorporating dictionary definitions. Traditional topic models treat words as surface strings without assuming predefined knowledge about word meaning. They infer topics only by observing surface word co-occurrence. However, the co-occurred words may not be semantically related in a manner that is relevant for topic coherence. Exploiting dictionary
definitions explicitly in our model yields a better understanding of word semantics leading to better text modeling. We exploit WordNet as a lexical resource for sense definitions. We show that explicitly modeling word definitions helps improve performance significantly over the baseline for a text categorization task
How much hybridisation does machine translation need?
This is the peer reviewed version of the following article: [Costa-jussà, M. R. (2015), How much hybridization does machine translation Need?. J Assn Inf Sci Tec, 66: 2160–2165. doi:10.1002/asi.23517], which has been published in final form at [10.1002/asi.23517]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Rule-based and corpus-based machine translation (MT)have coexisted for more than 20 years. Recently, bound-aries between the two paradigms have narrowed andhybrid approaches are gaining interest from bothacademia and businesses. However, since hybridapproaches involve the multidisciplinary interaction oflinguists, computer scientists, engineers, and informa-tion specialists, understandably a number of issuesexist.While statistical methods currently dominate researchwork in MT, most commercial MT systems are techni-cally hybrid systems. The research community shouldinvestigate the bene¿ts and questions surrounding thehybridization of MT systems more actively. This paperdiscusses various issues related to hybrid MT includingits origins, architectures, achievements, and frustra-tions experienced in the community. It can be said thatboth rule-based and corpus- based MT systems havebene¿ted from hybridization when effectively integrated.In fact, many of the current rule/corpus-based MTapproaches are already hybridized since they do includestatistics/rules at some point.Peer ReviewedPostprint (author's final draft
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations
We propose a novel data augmentation for labeled sentences called contextual
augmentation. We assume an invariance that sentences are natural even if the
words in the sentences are replaced with other words with paradigmatic
relations. We stochastically replace words with other words that are predicted
by a bi-directional language model at the word positions. Words predicted
according to a context are numerous but appropriate for the augmentation of the
original words. Furthermore, we retrofit a language model with a
label-conditional architecture, which allows the model to augment sentences
without breaking the label-compatibility. Through the experiments for six
various different text classification tasks, we demonstrate that the proposed
method improves classifiers based on the convolutional or recurrent neural
networks.Comment: NAACL 201
Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech
Polyphone disambiguation aims to capture accurate pronunciation knowledge
from natural text sequences for reliable Text-to-speech (TTS) systems. However,
previous approaches require substantial annotated training data and additional
efforts from language experts, making it difficult to extend high-quality
neural TTS systems to out-of-domain daily conversations and countless languages
worldwide. This paper tackles the polyphone disambiguation problem from a
concise and novel perspective: we propose Dict-TTS, a semantic-aware generative
text-to-speech model with an online website dictionary (the existing prior
information in the natural language). Specifically, we design a
semantics-to-pronunciation attention (S2PA) module to match the semantic
patterns between the input text sequence and the prior semantics in the
dictionary and obtain the corresponding pronunciations; The S2PA module can be
easily trained with the end-to-end TTS model without any annotated phoneme
labels. Experimental results in three languages show that our model outperforms
several strong baseline models in terms of pronunciation accuracy and improves
the prosody modeling of TTS systems. Further extensive analyses demonstrate
that each design in Dict-TTS is effective. The code is available at
\url{https://github.com/Zain-Jiang/Dict-TTS}.Comment: Accepted by NeurIPS 202
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