Digital product passports (DPP) are at the forefront of circular economy initiatives and green logistics. They can be defined as unique digital documents that memorize and disclose product sustainability information throughout the lifecycle. However, DPP challenges are substantial, and research is still in its early stages and fragmented in this vibrant field that promises to change product information flows at an international scale. This paper presents a concept-centric systematic literature review and a bibliometric analysis of 40 publications published since 2021. The main objective is to understand the research landscape of DPP and identify guidelines for DPP structure, technology, requirements, and challenges for DPP adoption. A research agenda for DPPs is also presented. For academics, this review highlights the most recent advances in DPP research and unsolved questions. Practitioners may find our review helpful in identifying information requirements, preparing their digital infrastructure for DPP creation, and anticipating their role in DPP construction with particularities for each supply chain.

This paper 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 multifeature 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).

To automatically evaluate the performance of children reading aloud or to follow a child’s reading in reading tutor applications, different types of reading disfluencies and mispronunciations must be accounted for. In this work, we aim to detect most of these disfluencies in sentence and pseudoword reading. Detecting incorrectly pronounced words, and quantifying the quality of word pronunciations, is arguably the hardest task. We approach the challenge as a two-step process. First, a segmentation using task-specific lattices is performed, while detecting repetitions and false starts and providing candidate segments for words. Then, candidates are classified as mispronounced or not, using multiple features derived from likelihood ratios based on phone decoding and forced alignment, as well as additional meta-information about the word. Several classifiers were explored (linear fit, neural networks, support vector machines) and trained after a feature selection stage to avoid overfitting. Improved results are obtained using feature combination compared to using only the log likelihood ratio of the reference word (22% versus 27% miss rate at constant 5% false alarm rate).

Reading aloud performance in children is typically assessed by teachers on an individual basis, manually marking reading time and incorrectly read words. A computational tool that assists with recording reading tasks, automatically analyzing them and providing performance metrics could be a significant help. Towards that goal, this work presents an approach to automatically predicting the overall reading aloud ability of primary school children (6-10 years old), based on the reading of sentences and pseudowords. The opinions of primary school teachers were gathered as ground truth of performance, who provided 0-5 scores closely related to the expectations at the end of each grade. To predict these scores automatically, features based on reading speed and number of disfluencies were extracted, after an automatic disfluency detection. Various regression models were trained, with Gaussian process regression giving best results for automatic features. Feature selection from both sentence and pseudoword reading tasks gave the closest predictions, with a correlation of 0.944. Compared to the use of manual annotation with the best correlation being 0.952, automatic annotation was only 0.8% worse. Furthermore, the error rate of predicted scores relative to ground truth was found to be smaller than the deviation of evaluators’ opinion per child.

The automatic evaluation of children's reading performance by detecting and analyzing errors and disfluencies in speech is an important tool to build automatic reading tutors and to complement the current method of manual evaluations of overall reading ability in schools. A large amount of speech from children reading aloud plentiful in errors and disfluencies is needed to train acoustic, disfluency and pronunciation models for an automatic reading assessment system. This paper describes the acquisition and analysis of a read-aloud speech database of European Portuguese from children aged 6-10 from the first to fourth school grades. Towards the goal of detecting all reading errors and disfluencies, we apply a decoding process to the utterances using flexible word level lattices that allow syllable based false starts and repetitions of two or more word sequences. The proposed method proved promising in detecting corrections and repetitions in sentences, and provides an improved alignment of the data, helpful for future annotation tasks. The analysis of the database also shows agreement to government defined curricular goals for reading.

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