A dynamic tonal perception model for optimal pitch stylization

Origlia, Antonio; Abete, Giovanni; Cutugno, Francesco

doi:10.1016/j.csl.2012.04.003

Cited by 13 publications

(4 citation statements)

References 13 publications

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“…Such adjustments can, however, only be made with access to the appropriate speech recordings and the appropriate clinical expert raters in the language and are not considered further in this report. The components of the processing pipeline are, however, widely available and well documented [36,42,44,73,74], removing any barrier to replication, language or disease adjustments in weights, and replication efforts in later research.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Dysprosody in Patients with Parkinson’s Disease

Nylén

2023

Preprint

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Dysprosody is a commonly described feature of speech deterioration due to Parkinson’s disease. Descriptions of the tonal movements underlying dysprosody have been attempted but has not afforded automation. The current study assessed a fully automated acoustic analysis pipeline in terms of its ability to predict human raters’ perception of dysprosody in patients with Parkinson’s disease. Read speech samples of 68 speakers with PD (45 male and 23 female) aged 65.0±9.8 years were assessed by four human clinical experts in terms of dysprosody severity. The recordings were also submitted to a speech processing pipeline in which portions with speech were identified and provided with a parametrization of intonation. Five models were trained and tuned based on 75% of utterances and their perceptual evaluations in a 10-fold cross-validation procedure and evaluated in terms of their ability to predict the perceptual assessments of recordings not included in the training. The performances of models were compared to human raters’ agreement with the majority vote on a speech sample. The results showed that human raters’ assessments of dysprosody can be approximated by the automated procedure. Variability in pitch does not adequately describe the level of dysprosody due to Parkinson’s disease.

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Section: Discussionmentioning

confidence: 99%

Automated Detection of Dysprosody in Patients with Parkinson’s Disease

Nylén

2023

Preprint

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“…Duration appears to be able to increase prominence scoring independently of pitch while a minimum value for duration appears to be necessary for higher pitch values to actually contribute to increasing prominence scoring. This makes sense as the human capability of discriminating pitch levels and movements depends on their duration [25], [26]. Figure 3 shows that this effect is stronger when pitch is compared to the nucleus duration, as pitch movements that are relevant for prominence are typically found in the nucleus.…”

Section: A Qualitative Analysismentioning

confidence: 96%

An adaptive neuro-fuzzy inference system for the qualitative study of perceptual prominence in linguistics

Vitiello

Cutugno

Wagner

et al. 2017

2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Abstract-This paper explores the applications of fuzzy logic inference systems as an instrument to perform linguistic analysis in the domain of prosodic prominence. Understanding how acoustic features interact to make a linguistic unit be perceived as more relevant than the surrounding ones is generally needed to study the cognitive processes needed for speech understanding. It also has technological applications in the field of speech recognition and synthesis. We present a first experiment to show how fuzzy inference systems, being characterised by their capability to provide detailed insight about the models obtained through supervised learning can help investigate the complex relationships among acoustic features linked to prominence perception.

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“…The pitch stylization routine is based on the OpS algorithm [21], which provides a linear approximation of the pitch curve motivated by psycholinguistics studies on pitch movements perception [12,13,14]. The resulting curve is used to provide an account of the mean pitch and its variability.…”

Section: Emotional Speech Modulementioning

confidence: 99%

CoWME

Calandra

Caso

Cutugno

et al. 2013

Proceedings of the 15th ACM on International Conference on Multimodal Interaction

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Evaluating human machine interaction in the case of multimodal systems is often a difficult task involving the monitoring of multiple sources, data fusion and results interpretation. While subtasks are highly dependent on the specific goal of the application and on the available interaction modalities, it is possible to formalize this workflow into a standard process and to consider a generic measure to estimate the ease of use of a specific application. In this work, we present CoWME, a modular software architecture describing multimodal human machine interaction evaluation, from data collection to final evaluation, in a formal way, in terms of cognitive workload. Communication protocols between modules are described in XML while data fusion is delegated to a configurable rule engine. An interface module is introduced between the monitoring modules and the rule engine to collect and summarize data streams for cognitive workload evaluation. We present a deployment example showing how this architecture is deployed by monitoring an interactive session with an Android application taking into account stressed speech detection, mydriasis and touch analysis.

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A dynamic tonal perception model for optimal pitch stylization

Cited by 13 publications

References 13 publications

Automated Detection of Dysprosody in Patients with Parkinson’s Disease

Automated Detection of Dysprosody in Patients with Parkinson’s Disease

An adaptive neuro-fuzzy inference system for the qualitative study of perceptual prominence in linguistics

CoWME

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