Browsing by Author "Abad, Alberto"
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- Assessment of Parkinson’s disease medication state through automatic speech analysisPublication . Pompili, Anna; Solera-Urena, Rubén; Abad, Alberto; Cardoso, Rita; Guimarães, Isabel; Fabbri, Margherita; Martins, Isabel; Ferreira, JoaquimParkinson’s disease (PD) is a progressive degenerative disorder of the central nervous system characterized by motor and nonmotor symptoms. As the disease progresses, patients alternate periods in which motor symptoms are mitigated due to medication intake (ON state) and periods with motor complications (OFF state). The time that patients spend in the OFF condition is currently the main parameter employed to assess pharmacological interventions and to evaluate the efficacy of different active principles. In this work, we present a system that combines automatic speech processing and deep learning techniques to classify the medication state of PD patients by leveraging personal speech-based bio-markers. We devise a speakerdependent approach and investigate the relevance of different acoustic-prosodic feature sets. Results show an accuracy of 90.54% in a test task with mixed speech and an accuracy of 95.27% in a semi-spontaneous speech task. Overall, the experimental assessment shows the potentials of this approach towards the development of reliable, remote daily monitoring and scheduling of medication intake of PD patients.
- Assessment of Parkinson’s disease medication state through automatic speech analysisPublication . Pompili, Anna; Solera-Urena, Rubén; Abad, Alberto; Cardoso, Rita; Guimarães, Isabel; Fabbri, Margherita; Martins, Isabel P; Ferreira, JoaquimParkinson’s disease (PD) is a progressive degenerative disorder of the central nervous system characterized by motor and nonmotor symptoms. As the disease progresses, patients alternate periods in which motor symptoms are mitigated due to medication intake (ON state) and periods with motor complications (OFF state). The time that patients spend in the OFF condition is currently the main parameter employed to assess pharmacological interventions and to evaluate the efficacy of different active principles. In this work, we present a system that combines automatic speech processing and deep learning techniques to classify the medication state of PD patients by leveraging personal speech-based bio-markers. We devise a speakerdependent approach and investigate the relevance of different acoustic-prosodic feature sets. Results show an accuracy of 90.54% in a test task with mixed speech and an accuracy of 95.27% in a semi-spontaneous speech task. Overall, the experimental assessment shows the potentials of this approach towards the development of reliable, remote daily monitoring and scheduling of medication intake of PD patients.
- Automatic detection of Parkinson’s disease: an experimental analysis of common speech production tasks used for diagnosisPublication . Pompili, Anna; Abad, Alberto; Romano, Paolo; Martins, Isabel P; Cardoso, Rita; Santos, Helena; Carvalho, Joana; Guimarães, Isabel; Ferreira, JoaquimParkinson’s disease (PD) is the second most common neurodegenerative disorder of mid-to-late life after Alzheimer’s disease. During the progression of the disease, most individuals with PD report impairments in speech due to deficits in phonation, articulation, prosody, and fluency. In the literature, several studies perform the automatic classification of speech of people with PD considering various types of acoustic information extracted from different speech tasks. Nevertheless, it is unclear which tasks are more important for an automatic classification of the disease. In this work, we compare the discriminant capabilities of eight verbal tasks designed to capture the major symptoms affecting speech. To this end, we introduce a new database of Portuguese speakers consisting of 65 healthy control and 75 PD subjects. For each task, an automatic classifier is built using feature sets and modeling approaches in compliance with the current state of the art. Experimental results permit to identify reading aloud prosodic sentences and story-telling tasks as the most useful for the automatic detection of PD.
- The BioVisualSpeech corpus of words with sibilants for speech therapy games developmentPublication . Cavaco, Sofia; Guimarães, Isabel; Ascensão, Mariana; Abad, Alberto; Anjos, Ivo; Oliveira, Francisco; Martins, Sofia; Marques, Nuno; Eskenazi, Maxine; Magalhães, João; Grilo, Ana MargaridaAbstract: 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.
- The BioVisualSpeech european portuguese sibilants corpusPublication . Grilo, Ana Margarida; Guimarães, Isabel; Ascensão, Mariana; Abad, Alberto; Anjos, Ivo; Magalhães, João; Cavaco, SofiaAbstract. The development of reliable speech therapy computer tools that automatically classify speech productions depends on the quality of the speech data set used to train the classi cation algorithms. The data set should characterize the population in terms of age, gender and native language, but it should also have other important properties that characterize the population that is going to use the tool. Thus, apart from including samples from correct speech productions, it should also have samples from people with speech disorders. Also, the annotation of the data should include information on whether the phonemes are correctly or wrongly pronounced. Here, we present a corpus of European Portuguese children's speech data that we are using in the development of speech classi ers for speech therapy tools for Portuguese children. The corpus includes data from children with speech disorders and in which the labelling includes information about the speech production errors. This corpus, which has data from 356 children from 5 to 9 years of age, focuses on the European Portuguese sibilant consonants and can be used to train speech recognition models for tools to assist the detection and therapy of sigmatism.