6 accepted papers
In education, analyzing student oral reading is critical to reading comprehension, vocabulary development, and fluency. Automating these assessments is a crucial part of language research– however, researchers often use a separate model for each assessment. Transferring knowledge between assessments…
Oral reading fluency assessment is a process where a student reads a passage aloud and is scored against words read correctly by a human listener. Current automatic reading fluency systems match these words read using speech recognition models trained with clean speech data from native adult speaker…
We introduce a differentiable statistical moment aggregation layer, enabling networks to learn the optimal method of statistical moment pooling for automatic modulation classification. Statistical pooling, a cornerstone of convolutional networks, consolidates activations into fixed-length representa…
Automated prosody classification in the context of oral reading fluency is a critical area for the objective evaluation of students’ reading proficiency. In this work, we present the largest dataset to date in this domain. It includes spoken phrases from over 1,300 students assessed by multiple trai…
Using spectral multiplication to compute convolution in neural networks has been investigated by a number of researchers because of its potential in speeding up computations for large images. However, previous methods require the learning of arbitrarily large convolution filters in the spectral doma…
Linear adaptive systems are a well-known staple in numerous signal processing applications. Recently, significant activity and performance gains have been achieved in multilayer neural networks for deep learning applied to practical data processing applications. In this paper, we describe the import…