Reliable assessment of oral reading fluency (ORF) is of great importance in foundational literacy missions globally. For the design of level appropriate testing passages, text difficulty has traditionally been based on coarse-grained measures of readability like the Flesch–Kincaid score. We present a novel study where we deploy psycholinguistic measures of reading difficulty from Natural Language Processing to predict the duration of words in Hindi read-aloud speech. We test the hypotheses that expectation-based measures of linguistic complexity are significant predictors of word duration in Hindi read-aloud speech. We validate the stated hypotheses by estimating surprisal measures inspired from Surprisal Theory of sentence comprehension and introduce a novel measure of orthographic complexity to model the intricacies of the Hindi script. Cognitive modelling experiments were conducted on a dataset of six Hindi short stories read aloud by 5 expert readers, containing 2 measures of word duration. Our results show that both surprisal as well as the orthographic complexity measures are significant predictors of word duration. In contrast with long words, we find duration reducing with increased orthographic complexity in the case of short words. The variation between individual speakers in terms of word duration is very low and the variance in the data is caused by the properties of the words used in the text. Finally, we reflect on the implications of our work for cognitive models of language production and for ORF assessment.

This work deploys linguistically motivated features to classify paragraph-level text into fiction and non-fiction genre using a logistic regression model and infers lexical and syntactic properties that distinguish the two genres. Previous works have focused on classifying document-level text into fiction and non-fiction genres, while in this work, we deal with shorter texts which are closer to real-world applications like sentiment analysis of tweets. Going beyond simple POS tag ratios proposed in Qureshi et al.(2019) for document-level classification, we extracted multiple linguistically motivated features belonging to four categories: Lexical features, POS ratio features, Syntactic features and Raw features. For the task of short-text classification, a model containing 28 best-features (selected via Recursive feature elimination with cross-validation; RFECV) confers an accuracy jump of 15.56 % over a baseline model consisting of 2 POS-ratio features found effective in previous work (cited above). The efficacy of the above model containing a linguistically motivated feature set also transfers over to another dataset viz, Baby BNC corpus. We also compared the classification accuracy of the logistic regression model with two deep-learning models. A 1D CNN model gives an increase of 2% accuracy over the logistic Regression classifier on both corpora. And the BERT-base-uncased model gives the best classification accuracy of 97% on Brown corpus and 98% on Baby BNC corpus. Although both the deep learning models give better results in terms of classification accuracy, the problem of interpreting these models remains unsolved. In contrast, regression model coefficients revealed that fiction texts tend to have more character-level diversity and have lower lexical density (quantified using content-function word ratios) compared to non-fiction texts. Moreover, subtle differences in word order exist between the two genres, i.e., in fiction texts Verbs precede Adverbs (inter-alia).

We test the hypothesis that discourse predictability influences Hindi syntactic choice.While prior work has shown that a number of factors (e.g., information status, dependency length, and syntactic surprisal) influence Hindi word order preferences, the role of discourse predictability is underexplored in the literature.Inspired by prior work on syntactic priming, we investigate how the words and syntactic structures in a sentence influence the word order of the following sentences.Specifically, we extract sentences from the Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those sentences, and build a classifier to predict which sentences actually occurred in the corpus against artificially generated distractors.The classifier uses a number of discourse-based features and cognitive features to make its predictions, including dependency length, surprisal, and information status.We find that information status and LSTM-based discourse predictability influence word order choices, especially for non-canonical objectfronted orders.We conclude by situating our results within the broader syntactic priming literature.

This paper presents an eye-tracking experiment comparing the processing of different accent patterns in unit selection synthesis and human speech. The synthetic speech results failed to replicate the facilitative effect of contextually appropriate accent patterns found with human speech, while producing a more robust intonational garden-path effect with contextually inappropriate patterns, both of which could be due to processing delays seen with the synthetic speech. As the synthetic speech was of high quality, the results indicate that eye tracking holds promise as a highly sensitive and objective method for the online evaluation of prosody in speech synthesis. Index Terms: speech synthesis, evaluation, prosody, eye tracking, unit selection