A Timing Model for Fast French

Models of speech timing are of both fundamental and applied interest. At the fundamental level, the prediction of time periods occupied by syllables and segments is required for general models of speech prosody and segmental structure. At the applied level, complete models of timing are an essential component of any speech synthesis system. Previous research has established that a large number of factors influence various levels of speech timing. Statistical analysis and modelling can identify order of importance and mutual influences between such factors. In the present study, a three-tiered model was created by a modified step-wise statistical procedure. It predicts the temporal structure of French, as produced by a single, highly fluent speaker at a fast speech rate (100 phonologically balanced sentences, hand-scored in the acoustic signal). The first tier models segmental influences due to phoneme type and contextual interactions between phoneme types. The second tier models syllable-level influences of lexical vs. grammatical status of the containing word, presence of schwa and the position within the word. The third tier models utterance-final lengthening. The complete segmental-syllabic model correlated with the original corpus of 1204 syllables at an overall r = 0.846. Residuals were normally distributed. An examination of subsets of the data set revealed some variation in the closeness of fit of the model. The results are considered to be useful for an initial timing model, particularly in a speech synthesis context. However, further research is required to extend the model to other speech rates and to examine inter-speaker variability in greater detail

An automated lexical stress classification tool for assessing dysprosody in childhood apraxia of speech

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated. Sixteen children with typical development (TD) and 26 with CAS produced 50 polysyllabic words. Words with strong–weak (SW, e.g., dinosaur) or WS (e.g., banana) stress were fed to the classification tool, and the accuracy measured (a) against expert judgment, (b) for speaker group, and (c) with/without prior knowledge of phonemic errors in the sample. The influence of segmental features and participant factors on tool accuracy was analysed. Linear mixed modelling showed significant interaction between group and stress type, surviving adjustment for age and CAS severity. For TD, agreement for SW and WS words was >80%, but CAS speech was higher for SW (>80%) than WS (~60%). Prior knowledge of segmental errors conferred no clear advantage. Automatic lexical stress classification shows promise for identifying errors in children’s speech at diagnosis or with treatment-related change, but accuracy for WS words in apraxic speech needs improvement. Further training of algorithms using larger sets of labelled data containing impaired speech and WS words may increase accuracy

Sustainability and Food: a Text Analysis of the Scientific Literature

The paper analyses the evolution of the research debate related to sustainability and to the relation between food and sustainability. A number of text analysis techniques were combined for the investigation of scientific papers. The results stress how discourse analysis of sustainability in the pre-Rio period is mostly associated with agriculture and with a vision where the ecological and environmental aspects are dominant. In the post-Rio phase, the discussion about sustainability, though still strongly linked to environmental issues, enters a holistic dimension that includes social elements. The themes of energy and the sustainability of urban areas become central, and the scientific debate stresses the importance of indicators within an assessment approach linked to the relevance of planning and intervention aspects. The focus on the role of food within the debate on sustainability highlights a food security oriented approach in the pre-Rio phase, with a particular attention towards agriculture and third world Countries. In the post-Rio period, the focus of the analysis moves towards developed Countries. Even though food security remains a strongly significant element of the debate, the attention shifts towards consumers and food choices

A computational simulation of children's performance across three nonword repetition tests

The nonword repetition test has been regularly used to examine children’s vocabulary acquisition, and yet there is no clear explanation of all of the effects seen in nonword repetition. This paper presents a study of 5-6 year-old children’s repetition performance on three nonword repetition tests that vary in the degree of their lexicality. EPAM-VOC, a model of children’s vocabulary acquisition, is then presented that captures the children’s performance in all three repetition tests. The model represents a clear explanation of how working memory and long-term lexical and sub-lexical knowledge interact in a way that is able to simulate repetition performance across three nonword tests within the same model and without the need for test specific parameter settings

Prosodic Event Recognition using Convolutional Neural Networks with Context Information

This paper demonstrates the potential of convolutional neural networks (CNN) for detecting and classifying prosodic events on words, specifically pitch accents and phrase boundary tones, from frame-based acoustic features. Typical approaches use not only feature representations of the word in question but also its surrounding context. We show that adding position features indicating the current word benefits the CNN. In addition, this paper discusses the generalization from a speaker-dependent modelling approach to a speaker-independent setup. The proposed method is simple and efficient and yields strong results not only in speaker-dependent but also speaker-independent cases.Comment: Interspeech 2017 4 pages, 1 figur