Many accounts of the processing of morphologically complex words have been elaborated within the interactive activation model of word identification. Conceptually, this model adopts a "segmentation-through-recognition" approach to morphological decomposition, which assumes that a complex word activates representations of constituent morphemes as well as the representation of the whole word. How...

This paper describes the system for the recognition of French handwriting submitted by A2iA to the competition organized at ICDAR2011 using the Rimes database. This system is composed of several recognizers based on three di erent recognition technologies, combined using a novel combination method. A framework for multiword recognition based on weighted nite state transducers is presented, usin...

In this paper, we present an automatic speech recognition (ASR) system based on the combination of an automatic phone recogniser and a computational model of human speech recognition SpeM that is capable of computing ‘word activations’ during the recognition process, in addition to doing normal speech recognition, a task in which conventional ASR architectures only provide output after the end ...

A method is presented to filter the output of a word recognition algorithm, which may contain errors, to locate decisions that should be correct with a high degree of certainty. The algorithm uses the output of a word recognition system and a vector space model for information retrieval to locate a set of documents that have topics which are similar to that of the input document. The vocabulary...

In this paper, we present a new strategy for unsupervised speaker adaptation. In our approach, the adaptation is performed in two steps for each test utterance. In the first online step, we utilize speaker and gender identification, a set of speaker dependent (SD) hidden Markov models (HMMs) and our own fast linear model combination approach to create a proper model for the first speech recogni...

This paper describes a model which enables a speech recognition system to automatically detect new words and to provide a rough phonetic transcription. In our approach to the new word problem the decision whether new words occurred in the speech input is not based exclusively on acoustic evidence but also on a language model designed to support the detection of new words. We describe preliminar...

Because speakers do not produce uninflected or 'base' forms, and listeners do not hear them, the shape of the word lexicon in languages with highly productive word formation processes directly addresses the conflict between morphological theories which assume the primacy of word formation processes (Anderson 1992, Bybee and Moder 1983,) and theories of word recognition such as the Cohort theory...

Holistic word recognition attempts to recognize the entire word image as a single pattern. In general, it performs better than segmentation based word recognition model for known, fixed and small sized lexicon. The present work deals with recognition of handwritten words in Hindi in holistic way. Features like area, aspect ratio, density, pixel ratio, longest run, centroid and projection length...

Word category prediction is used to implement an accurate word recognition system. Traditional statistical approaches require considerable training data to estimate the probabilities of word sequences, and many parameters to memorize probabilities. To solve this problem, NETgram, which is the neural network for word category prediction, is proposed. Training results show that the perfornmnce of...

So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly studied individually. Recently, a growing interest built for unified tackling the above jobs concurrently one single model. Current best-performing methods mainly include span-based sequence-to-sequence models, where unfortunately fo...