Validation of the Reading Tendency Index in school-age children: Replication with a bilingual sample

Defining deficits in reading ability may be accomplished through the analysis of a child’s reading tendencies, representing a possible paradigm shift in the conceptualization and assessment of reading disabilities. Based on this premise, Mohl and colleagues (2018) developed a quantitative paradigm to measure reading tendency in children through performance on two lexical decision tasks (LDTs) that differentially rely on decoding and sightword reading abilities. The Reading Tendency Index (RTI; Mohl et al., 2018) is calculated from the differential between drift rates on the phonologic and orthographic LDTs. Scores closer to zero represent a balanced approach whereas scores as a negative or positive value suggest the tendency to rely on phonological decoding or sightword reading strategies, respectively. It was suggested that a balanced approach promotes more proficient reading abilities; however, this original study was performed with a small, male-only sample with a significant number of children with an ADHD diagnosis. The present study provided independent examination of the RTI paradigm, including the two LDT tasks and original calculations, to validate the tasks as a measure of reading abilities in a larger, representative sample of school-aged children. The present study involved the following goals: 1) to replicate the three-group reading tendency structure based on LDT performance in a larger representative sample of school-aged children, 2) to examine the construct validity of the RTI groupings and LDT tasks as a quantitative measure of reading ability, 3) to determine whether RTI group membership can be predicted based on reading and other cognitive skills, and 4) to explore performance differences, if any, in participants enrolled in French Immersion programs. The final sample included 92 participants aged 7 to 14 years (Mage = 9.96 years) recruited from English (n = 49) and French Immersion (n = 43) schools. Results indicated the following: 1) the three-group RTI structure was replicated in the larger sample of typically-developing school-aged children; 2) Sightword Readers had poorer performance on reading fluency, reading comprehension, and spelling than Balanced Readers and Decoders, but groups did not differ otherwise; 3) only reading comprehension predicted membership for the Sightword group; and 4) French Immersion students demonstrated similar patterns of performance on the RTI and other cognitive measures as English-only students. Supplemental post-hoc analyses were performed to explore different cut-off scores and methods for determining RTI groups. Implications and limitations of the current findings as well as considerations for future studies are discussed

Connectionist probability estimators in HMM speech recognition

The authors are concerned with integrating connectionist networks into a hidden Markov model (HMM) speech recognition system. This is achieved through a statistical interpretation of connectionist networks as probability estimators. They review the basis of HMM speech recognition and point out the possible benefits of incorporating connectionist networks. Issues necessary to the construction of a connectionist HMM recognition system are discussed, including choice of connectionist probability estimator. They describe the performance of such a system using a multilayer perceptron probability estimator evaluated on the speaker-independent DARPA Resource Management database. In conclusion, they show that a connectionist component improves a state-of-the-art HMM system

Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks

Millions of people around the world are diagnosed with neurological disorders like Parkinson’s, Cerebral Palsy or Amyotrophic Lateral Sclerosis. Due to the neurological damage as the disease progresses, the person suffering from the disease loses control of muscles, along with speech deterioration. Speech deterioration is due to neuro motor condition that limits manipulation of the articulators of the vocal tract, the condition collectively called as dysarthria. Even though dysarthric speech is grammatically and syntactically correct, it is difficult for humans to understand and for Automatic Speech Recognition (ASR) systems to decipher. With the emergence of deep learning, speech recognition systems have improved a lot compared to traditional speech recognition systems, which use sophisticated preprocessing techniques to extract speech features. In this digital era there are still many documents that are handwritten many of which need to be digitized. Offline handwriting recognition involves recognizing handwritten characters from images of handwritten text (i.e. scanned documents). This is an interesting task as it involves sequence learning with computer vision. The task is more difficult than Optical Character Recognition (OCR), because handwritten letters can be written in virtually infinite different styles. This thesis proposes exploiting deep learning techniques like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) for offline handwriting recognition. For speech recognition, we compare traditional methods for speech recognition with recent deep learning methods. Also, we apply speaker adaptation methods both at feature level and at parameter level to improve recognition of dysarthric speech