Design and analysis of a database to evaluate children’s reading aloud performance

To evaluate the reading performance of children, human assessment is usually involved, where a teacher or tutor has to take time to individually estimate the performance in terms of fluency (speed, accuracy and expression). Automatic estimation of reading ability can be an important alternative or complement to the usual methods, and can improve other applications such as e-learning. Techniques must be developed to analyse audio recordings of read utterances by children and detect the deviations from the intended correct reading i.e. disfluencies. For that goal, a database of 284 European Portuguese children from 6 to 10 years old (1st–4th grades) reading aloud amounting to 20 h was collected in private and public Portuguese schools. This paper describes the design of the reading tasks as well as the data collection procedure. The presence of different types of disfluencies is analysed as well as reading performance compared to known curricular goals.info:eu-repo/semantics/acceptedVersio

The BioVisualSpeech corpus of words with sibilants for speech therapy games development

Abstract: In order to develop computer tools for speech therapy that reliably classify speech productions, there is a need for speech production corpora that characterize the target population in terms of age, gender, and native language. Apart from including correct speech productions, in order to characterize the target population, the corpora should also include samples from people with speech sound disorders. In addition, the annotation of the data should include information on the correctness of the speech productions. Following these criteria, we collected a corpus that can be used to develop computer tools for speech and language therapy of Portuguese children with sigmatism. The proposed corpus contains European Portuguese children’s word productions in which the words have sibilant consonants. The corpus has productions from 356 children from 5 to 9 years of age. Some important characteristics of this corpus, that are relevant to speech and language therapy and computer science research, are that (1) the corpus includes data from children with speech sound disorders; and (2) the productions were annotated according to the criteria of speech and language pathologists, and have information about the speech production errors. These are relevant features for the development and assessment of speech processing tools for speech therapy of Portuguese children. In addition, as an illustration on how to use the corpus, we present three speech therapy games that use a convolutional neural network sibilants classifier trained with data from this corpus and a word recognition module trained on additional children data and calibrated and evaluated with the collected corpus.info:eu-repo/semantics/publishedVersio

Mispronunciation Detection in Children's Reading of Sentences

This work proposes an approach to automatically parse children’s reading of sentences by detecting word pronunciations and extra content, and to classify words as correctly or incorrectly pronounced. This approach can be directly helpful for automatic assessment of reading level or for automatic reading tutors, where a correct reading must be identified. We propose a first segmentation stage to locate candidate word pronunciations based on allowing repetitions and false starts of a word’s syllables. A decoding grammar based solely on syllables allows silence to appear during a word pronunciation. At a second stage, word candidates are classified as mispronounced or not. The feature that best classifies mispronunciations is found to be the log-likelihood ratio between a free phone loop and a word spotting model in the very close vicinity of the candidate segmentation. Additional features are combined in multi-feature models to further improve classification, including: normalizations of the log-likelihood ratio, derivations from phone likelihoods, and Levenshtein distances between the correct pronunciation and recognized phonemes through two phoneme recognition approaches. Results show that most extra events were detected (close to 2% word error rate achieved) and that using automatic segmentation for mispronunciation classification approaches the performance of manual segmentation. Although the log-likelihood ratio from a spotting approach is already a good metric to classify word pronunciations, the combination of additional features provides a relative reduction of the miss rate of 18% (from 34.03% to 27.79% using manual segmentation and from 35.58% to 29.35% using automatic segmentation, at constant 5% false alarm rate).info:eu-repo/semantics/publishedVersio