A unified method for augmented incremental recognition of online handwritten Japanese and English text

We present a unifed method to augmented incremental recognition for online handwritten Japanese and English text, which is used for busy or on-the-fly recognition while writing, and lazy or delayed recognition after writing, without incurring long waiting times. It extends the local context for segmentation and recognition to a range of recent strokes called "segmentation scope" and "recognition scop", respectively. The recognition scope is inside of the segmentation scope. The augmented incremental recognition triggers recognition at every several recent strokes, updates the segmentation and recognition candidate lattice, and searches over the lattice for the best result incrementally. It also incorporates three techniques. The frst is to reuse the segmentation and recognition candidate lattice in the previous recognition scope for the current recognition scope. The second is to fx undecided segmentation points if they are stable between character/word patterns. The third is to skip recognition of partial candidate character/word patterns. The augmented incremental method includes the case of triggering recognition at every new stroke with the above-mentioned techniques. Experiments conducted on TUAT-Kondate and IAM online database show its superiority to batch recognition (recognizing text at one time) and pure incremental recognition (recognizing text at every input stroke) in processing time, waiting time, and recognition accuracy

Why not model spoken word recognition instead of phoneme monitoring?

Norris, McQueen & Cutler present a detailed account of the decision stage of the phoneme monitoring task. However, we question whether this contributes to our understanding of the speech recognition process itself, and we fail to see why phonotactic knowledge is playing a role in phoneme recognition.

Speech technologies for the audiovisual and multimedia interaction environments

361 p

Effects of Orthographic, Phonologic, and Semantic Information Sources on Visual and Auditory Lexical Decision

The present study was designed to compare lexical decision latencies in visual and auditory modalities to three word types: (a) words that are inconsistent with two information sources, orthography and semantics (i.e., heterographic homophones such as bite/byte), (b) words that are inconsistent with one information source, semantics (i.e., homographic homophones such as bat), and (c) control words that are not inconsistent with any information source. Participants (N = 76) were randomly assigned to either the visual or auditory condition in which they judged the lexical status (word or nonword) of 180 words (60 heterographic homophones, 60 homographic homophones, and 60 control words) and 180 pronounceable nonsense word foils. Results differed significantly in the visual and auditory modalities. In visual lexical decision, homographic homophones were responded to faster than heterographic homophones or control words, which did not differ significantly. In auditory lexical decision, both homographic homophones and heterographic homophones were responded to faster than control words. Results are used to propose potential modifications to the Cooperative Division of Labor Model of Word Recognition (Harm & Seidenberg, 2004) to enable it to encompass both the visual and auditory modalities and account for the present results

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail