About: Word recognition is a research topic. Over the lifetime, 10826 publications have been published within this topic receiving 470251 citations. The topic is also known as: word reading.
Papers published on a yearly basis
TL;DR: In this article, several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system, and the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations.
Abstract: Several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary included many phonetically similar monosyllabic words, therefore the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations. For each parameter set (based on a mel-frequency cepstrum, a linear frequency cepstrum, a linear prediction cepstrum, a linear prediction spectrum, or a set of reflection coefficients), word templates were generated using an efficient dynamic warping method, and test data were time registered with the templates. A set of ten mel-frequency cepstrum coefficients computed every 6.4 ms resulted in the best performance, namely 96.5 percent and 95.0 percent recognition with each of two speakers. The superior performance of the mel-frequency cepstrum coefficients may be attributed to the fact that they better represent the perceptually relevant aspects of the short-term speech spectrum.
TL;DR: A parallel distributed processing model of visual word recognition and pronunciation is described, which consists of sets of orthographic and phonological units and an interlevel of hidden units and which early in the learning phase corresponds to that of children acquiring word recognition skills.
Abstract: A parallel distributed processing model of visual word recognition and pronunciation is described. The model consists of sets of orthographic and phonological units and an interlevel of hidden units. Weights on connections between units were modified during a training phase using the back-propagation learning algorithm. The model simulates many aspects of human performance, including (a) differences between words in terms of processing difficulty, (b) pronunciation of novel items, (c) differences between readers in terms of word recognition skill, (d) transitions from beginning to skilled reading, and (e) differences in performance on lexical decision and naming tasks. The model's behavior early in the learning phase corresponds to that of children acquiring word recognition skills. Training with a smaller number of hidden units produces output characteristic of many dyslexic readers. Naming is simulated without pronunciation rules, and lexical decisions are simulated without accessing word-level representations. The performance of the model is largely determined by three factors: the nature of the input, a significant fragment of written English; the learning rule, which encodes the implicit structure of the orthography in the weights on connections; and the architecture of the system, which influences the scope of what can be learned.
TL;DR: The DRC model is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud.
Abstract: This article describes the Dual Route Cascaded (DRC) model, a computational model of visual word recognition and reading aloud. The DRC is a computational realization of the dual-route theory of reading, and is the only computational model of reading that can perform the 2 tasks most commonly used to study reading: lexical decision and reading aloud. For both tasks, the authors show that a wide variety of variables that influence human latencies influence the DRC model's latencies in exactly the same way. The DRC model simulates a number of such effects that other computational models of reading do not, but there appear to be no effects that any other current computational model of reading can simulate but that the DRC model cannot. The authors conclude that the DRC model is the most successful of the existing computational models of reading.
TL;DR: The nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms are described.
Abstract: Handwriting has continued to persist as a means of communication and recording information in day-to-day life even with the introduction of new technologies. Given its ubiquity in human transactions, machine recognition of handwriting has practical significance, as in reading handwritten notes in a PDA, in postal addresses on envelopes, in amounts in bank checks, in handwritten fields in forms, etc. This overview describes the nature of handwritten language, how it is transduced into electronic data, and the basic concepts behind written language recognition algorithms. Both the online case (which pertains to the availability of trajectory data during writing) and the off-line case (which pertains to scanned images) are considered. Algorithms for preprocessing, character and word recognition, and performance with practical systems are indicated. Other fields of application, like signature verification, writer authentification, handwriting learning tools are also considered.
TL;DR: This paper elaborates the self-teaching hypothesis, reviews relevant evidence, and notes that current models of word recognition fail to address the quintessential problem of reading acquisition-independent generation of target pronunciations for novel orthographic strings.
Abstract: The self-teaching hypothesis proposes that phonological recoding functions as a self-teaching mechanism enabling the learner to independently acquire an autonomous orthographic lexicon. Successful decoding encounters with novel letter strings provide opportunities to learn word-specific print-to-meaning connections. Although it may not play a central role in skilled word recognition, phonological recoding, by virtue of its self-teaching function, is regarded as critical to successful reading acquisition. This paper elaborates the self-teaching hypothesis proposed by Jorm and Share (1983), and reviews relevant evidence. Key features of phonological recoding include an item-based rather than stage-based role in development, the progressive "lexicalization" of the process of recoding, and the importance of phonological awareness and contextual information in resolving decoding ambiguity. Although phonological skills have been shown to be primary in reading acquisition, orthographic processing appears to be an important but secondary source of individual differences. This implies an asymmetrical pattern of dissociations in both developmental and acquired reading disorders. Strong relationships between word recognition, basic phonological processing abilities and phonemic awareness are also consistent with the self-teaching notion. Finally, it is noted that current models of word recognition (both PDP and dual-route) fail to address the quintessential problem of reading acquisition-independent generation of target pronunciations for novel orthographic strings.
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