Minimizing word error rate in a dyslexic reading-oriented ASR engine using phoneme refinement and alternative pronunciation

Little attention has been given to detecting miscues in the text space read by dyslexic children over an automatic speech recognition (ASR) engine. In an ASR system, the miscues are represented by word error rate (WER) and miscue detection rate (MDR). At all time, WER must be kept low, and MDR high so as to achieve better recognition. This paper focus on minimizing word error rate by formulating a better model for perspicuous representation of input data. Such representation takes into account phoneme refinement and alternative pronunciation for a particular Bahasa Melayu (BM) speech data uttered by dyslexic children. Based on literature, a few other optimal models of input data and their recognition results were compared. It is found that phoneme refinement and alternative pronunciation produced better recognition results as evidenced in the performance metrics --lower WER and higher MDR-- which are 25% and 80.77% respectively

Automatic Speech Recognition Model for Dyslexic Children Reading in Bahasa Melayu

Dyslexic children suffer from dyslexia, a condition that profoundly impedes reading and spelling ability due to its phonological origin. Often, these children found reading and spelling difficult, exhaustive, and less interesting, and thus they are self-withdrawn from the learning process. When reading and spelling, they make many mistakes even for simple, common words that they themselves found embarrassing. However, this does not mean that they have lower IQ level than normal children. In fact, dyslexic children have average or high level of IQ and thus have a lot of potential when given the right help and support such as motivational support and suitable teaching techniques. With advancement in technology in education, computer-based applications are used to stimulate the learning process of reading and spelling. Hence, this study is an initiative towards proposing an automatic speech recognition (ASR) model to enable computer to 'listen' should incorrect reading occurs. The scope of this study focuses on modeling and recognizing single Bahasa Melayu (BM) words within the school syllabus for level one (tahap satu) dyslexic pupils of primary schools. To propose the ASR model, a reading and spelling model of dyslexic children reading in BM is first proposed, which models reading at word recognition level. To propose such model, ethnographic techniques are employed namely informal interviews and observation, in order to obtain the reading and spelling error patterns of dyslexic children. A number of ten dyslexic children, aged between 7 to 14 years old whose reading level is similar, participated in the study. These children are recruited from two public schools that offer special dyslexia classes for the children. A total of 6112 utterances are recorded in audio form resulting in a total of 6051 errors of various types. Among these, the patterns that are most frequently made by these children are of 'Substitutes vowel', 'omits consonant', 'nasals', and 'substitutes consonant'. The ASR model is proposed taking into consideration the error patterns that make lexical model a fundamental element for speech recognition. The lexical model is modeled to treat mispronunciations as alternative pronunciations or variants of target words. To that, a phoneme refinement strategy is applied aiming to increase recognition accuracy. A prototype recognizer is developed based on the proposed model for further evaluation. The evaluation is performed to evaluate the recognizer's performance in terms of accuracy, measured in word error rate (WER) and miscue detection rate (MDR) that is closely related to false alarm rate (FAR). The recognizer scores a satisfying 25% of WER and a relatively high MDR of 80.77% with 16.67% FAR

Dyslexic children's reading pattern as input for ASR: Data, analysis, and pronunciation model

To realize an automatic speech recognition (ASR) model that is able to recognize the Bahasa Melayu reading difficulties of dyslexic children, the language corpora has to be generated beforehand. For this purpose, data collection is performed in two public schools involving ten dyslexic children aged between seven to fourteen years old. A total of 114 Bahasa Melayu words,representing 23 consonant-vowel patterns in the spelling system of the language, served as the stimuli. The patterns range from simple to somewhat complex formations of consonant-vowel pairs in words listed in a level one primary school syllabus. An analysis was performed aimed at identifying the most frequent errors made by these dyslexic children when reading aloud, and describing the emerging reading pattern of dyslexic children in general. This paper hence provides an overview of the entire process from data collection to analysis to modeling the pronunciations of words which will serve as the active lexicon for the ASR model. This paper also highlights the challenges of data collection involving dyslexic children when they are reading aloud, and other factors that contribute to the complex nature of the data collected

AI planning for automating web service composition in tourism domain

Web services are changing the way how online business operates, especially in tourism domain. Typically, existing Web services are built individually as atomic services. The rapid growth of Web services has created the need for Web service composition so that clients can compose atomic services to achieve more complex tasks. Thus, to ease the process, automation is important. Automation means that the service composition is done with less or no user interference. Hence, we propose a framework to automatically compose Web services using SHOP2 planner. SHOP2 is a planner that implements AI planning technique, called Hi-erarchical Task Network (HTN). We propose and implement a framework to com-pose services available from the Australian Tourism Data Warehouse (ATDW) and present the example execution results. We also outline some drawbacks of our approach, identify open problems, and suggest future work to improve the framework

ASR technology for immediate intervention to support reading for dyslexic children

REading is an essential skill towards literacy development and thus help should be provided so that children can master the skill at early age. For dyslexic children, mastering the skills is a challenge. It has been widely agreed that the theory behind such difficulties in reading for dyslexic lies in the phonological-core deficits. Support has been given in many ways to dyslexic children to teach them to read from teaching using various multi-sensory methods to using computer-based applications which include animated characters and text-to-speech (TTS) technology. In such applications, although stimulating, requires the children to call for help by pressing custom-made buttons on the computer screen. Often, such an application requires the dyslexic children to be aware of their mistakes and be able to judge when help is needed. They too are just reluctant to ask the computer for help. Hence, such technology does not provide immediate intervention to correct any reading failure. It is therefore worth to look at the promising automatic speech recognition technology (ASR) to provide such intervention. Hence, this paper gives an overview of the use of ASR to facilitate immediate reading intervention which is the key element of remediating reading among dyslexic children. For such intervention to work, data on reading mistakes and patterns are observed and collected in audio format. The data serve as training and testing samples for an ASR to train on. An observation was carried out in two public schools participated in the study to record dyslexic children’s reading in Bahasa Melayu (BM) and observe error patterns and their behaviours toward reading. A total of 10 dyslexic children are involved and a total of 6384 utterances from a set of selected words have been gathered and analysed. Data are grouped into error type categories and the analysis performed gives ‘vowel substitution’ as the most frequent error made (20%). The significant findings can be of interest of special education teachers or parents to devise and use suitable approach to correct reading mistakes often made by dyslexic children. The findings also contribute to the development of a suitable and well-tuned ASR model focusing on dyslexic children reading aloud in BM

Pronunciation variations and context-dependent model to improve ASR performance for dyslexic children’s read speech

Focusing on the key element for an ASR-based application for dyslexic children reading isolated words in Bahasa Melayu, this paper can be an evidence of the need to have a carefully designed acoustic model for a satisfying recognition accuracy of 79.17% on test dataset. Pronunciation variations and context-dependent model are two main components of such acoustic model. This model adopts the most frequent errors in reading selected vocabulary, which are obtained from primary data collection and analysis.The analysis gives the most frequent spelling and reading errors as vowel substitution with over 20% of total errors made

Behavior usage model to manage the best practice of e-learning

This study aims to find e-Learning users’ behavior model that use data mining techniques to predict the successful learning behavior in utilizing e-Learning systems and to develop appropriate e-Learning users’ behavior models that could be used broadly in other higher institutions. Due to the lack of suitable e-Learning user’s behavior model for open source e-Learning system (Moodle) that could not be able to make a prediction for learning outcomes or performances.In this case, it is not useful enough for improving learners’ performance which may cause failure in learning.Therefore, this research is conducted upon three main phases, which are data preparation, data extraction and model verification for generating a verification pattern. This pattern could be used as a direction for creating a more appropriate e-Learning users’ behavior model

Document clustering for knowledge discovery using nature-inspired algorithm

As the internet is overload with information, various knowledge based systems are now equipped with data analytics features that facilitate knowledge discovery.This includes the utilization of optimization algorithms that mimics the behavior of insects or animals.This paper presents an experiment on document clustering utilizing the Gravitation Firefly algorithm (GFA).The advantage of GFA is that clustering can be performed without a pre-defined value of k clusters.GFA determines the center of clusters by identifying documents with high force.Upon identification of the centers, clusters are created based on cosine similarity measurement.Experimental results demonstrated that GFA utilizing a random positioning of documents outperforms existing clustering algorithm such as Particles Swarm Optimization (PSO) and K-means

Interaction design for dyslexic children reading application: A guideline

This paper outlines and explains the guideline needed to design an effective interaction design (IxD) for dyslexic children’s reading application.The guideline is developed based on theories that underly dyslexia and its effects towards reading, with emphasis given to the visual related theories and phonological deficit theory and core-affect theory.The needs of a dyslexic child to read properly and correctly with understanding of the related theories inspires the development of this guideline as it is aimed to aid the process of learning to read by facilitating them with useful design.Tested on a number of dyslexic children, the design seems to reduce their memory load for this particular task and thus reduce their difficulties in reading.Hence the role of an interaction designer is needed to answer the whats and hows and to design an interactive product (in this case–reading applications) to help dyslexic children to read

An IxD support model with affective characteristics for dyslexic childrens's reading application

This paper listed affective attributes of an interaction design for a reading application meant for dyslexic children. Different reading styles and unique reading approach of these specific children has long been a challenge for designers to come out with an acceptable interaction design (IxD). Emotional characteristics towards reading such as likes, dislikes, motivation, and satisfaction, which are very much related to affection, are essential in designing the suitable reading application to help them in learning to read and increase their interest in reading. A series of observation and unstructured interviews were conducted involving 28 dyslexic children, whose age range from 7 to 14 years old. The finding reveals that we can combine the affective attributes of the dyslexic children with the interaction model based on Norman’s work to map specific requirements suitable to dyslexic children’s reading ability. This inventive IxD model is proposed for readers with dyslexia. Additionally this paper shows how we translated such model into an automatic reading tutor for special need learners