Autism Spectrum Disorders (ASD) are a group of complex developmental conditions whose effects and severity show high intraindividual variability. However, one of the main symptoms shared along the spectrum is social interaction impairments that can be explored through acoustic analysis of speech production. In this paper, we compare 14 Italian-speaking children with ASD and 14 typically developing peers. Accordingly, we extracted and selected the acoustic features related to prosody, quality of voice, loudness, and spectral distribution using the parameter set eGeMAPS provided by the openSMILE feature extraction toolkit. We implemented four supervised machine learning methods to evaluate the extraction performances. Our findings show that Decision Trees (DTs) and Support Vector Machines (SVMs) are the best-performing methods. The overall DT models reach a 100% recall on all the trials, meaning they correctly recognise autistic features. However, half of its models overfit, while SVMs are more consistent. One of the results of the work is the creation of a speech pipeline to extract Italian speech biomarkers typical of ASD by comparing our results with studies based on other languages. A better understanding of this topic can support clinicians in diagnosing the disorder.

Digital Linguistic Biomarkers extracted from spontaneous language productions proved to be very useful for the early detection of various mental disorders. This paper presents a computational pipeline for the automatic processing of oral and written texts: the tool enables the computation of a rich set of linguistic features at the acoustic, rhythmic, lexical, and morphosyntactic levels. Several applications of the instrument - for the detection of Mild Cognitive Impairments, Anorexia Nervosa, and Developmental Language Disorders - are also briefly discussed.

This paper presents some preliminary results of the OPLON project. It aimed at identifying early linguistic symptoms of cognitive decline in the elderly. This pilot study was conducted on a corpus composed of spontaneous speech sample collected from 39 subjects, who underwent a neuropsychological screening for visuo-spatial abilities, memory, language, executive functions and attention. A rich set of linguistic features was extracted from the digitalised utterances (at phonetic, suprasegmental, lexical, morphological and syntactic levels) and the statistical significance in pinpointing the pathological process was measured. Our results show remarkable trends for what concerns both the linguistic traits selection and the automatic classifiers building.

Action verbs have many meanings, covering actions in different ontological types. Moreover, each language categorizes action in its own way. One verb can refer to many different actions and one action can be identified by more than one verb. The range of variations within and across languages is largely unknown, causing trouble for natural language processing tasks. IMAGACT is a corpus-based ontology of action concepts, derived from English and Italian spontaneous speech corpora, which makes use of the universal language of images to identify the different action types extended by verbs referring to action in English, Italian, Chinese and Spanish. This paper presents the infrastructure and the various linguistic information the user can derive from it. IMAGACT makes explicit the variation of meaning of action verbs within one language and allows comparisons of verb variations within and across languages. Because the action concepts are represented with videos, extension into new languages beyond those presently implemented in IMAGACT is done using competence-based judgments by mother-tongue informants without intense lexicographic work involving underdetermined semantic description

Regularities in position and level of prosodic prominences associated to patterns of Information Structure are identified for some Italian varieties. The experiments' results suggest a possibly new structural hypothesis on the role and function of the main prominence in marking information patterns. (1) An abstract and merely structural, """"topologic"""" concept of Prominence location can be conceived of, as endowed with the function of demarcation between units, before their culmination and """"description"""". This may suffice to explain much of the process by which speakers interpret the IS of utterances in discourse. Further features, such as the specific intonational contours of the different IS units, may thus represent a certain amount of redundancy. (2) Real utterances do not always signal the distribution of Topic and Focus clearly. Acoustically, many remain underspecified in this respect. This is especially true for the distinction between Topic-Focus and Broad Focus, which indeed often has no serious effects on the progression of communicative dynamism in the subsequent discourse. (3) The consistency of such results with the law of least effort, and the very high percent of matching between perceptual evaluations and automatic measurement, seem to validate the used algorithm.

Action verbs, which are highly frequent in speech, cause disambiguation problems that are relevant to Language Technologies. This is a consequence of the peculiar way each natural language categorizes Action i.e. it is a consequence of semantic factors. Action verbs are frequently “general”, since they extend productively to actions belonging to different ontological types. Moreover, each language categorizes action in its own way and therefore the cross-linguistic reference to everyday activities is puzzling. This paper briefly sketches the IMAGACT project, which aims at setting up a cross-linguistic Ontology of Action for grounding disambiguation tasks in this crucial area of the lexicon. The project derives information on the actual variation of action verbs in English and Italian from spontaneous speech corpora, where references to action are high in frequency. Crucially it makes use of the universal language of images to identify action types, avoiding the underdeterminacy of semantic definitions. Action concept entries are implemented as prototypic scenes; this will make it easier to extend the Ontology to other languages.