Lexical Tone Recognition
Lexical Tone Recognition - Related Videos
08:08
Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese
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Cited by 8 •
2016
Researchers adopt both the lexical decision task and the naming task to investigate some important topics such as character/word recognition by comparing the frequency effect between these two tasks. This article introduces this approach through two exemplar experiments and elaborates on the underlying logic.
06:48
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Cited by 6 •
2019
This article describes how to implement a simple lexical decision experiment to assess written word recognition in neurologically healthy participants and in individuals with dementia and cognitive decline. We also provide a detailed description of reaction time analysis using principal components analysis (PCA) and mixed-effects modeling.
10:41
Method to Measure Tone of Axial and Proximal Muscle
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Cited by 5 •
2011
We have developed a device (Twister) to study the regulation of tonic muscle activity during active postural maintenance. Twister measures torsional resistance and muscular responses in standing subjects during twisting of the body axis. The device can be flexibly configured to study various aspects of tonic control across the neck, trunk, and/or hips.
01:12
Alterations in Muscle Tone ll
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2026
Alterations in muscle tone are common manifestations of neurological disorders and reflect dysfunction within different nervous system regions. Spasticity, paratonia, and dystonia represent distinct forms of hypertonia, each with unique mechanisms, clinical features, and diagnostic importance.CharacteristicsSpasticity happens from upper motor neuron lesions and is characterized by velocity-dependent resistance to passive movement. Clinical features include:Exaggerated deep tendon reflexesClonus...
11:55
Physiological Correlates of Emotion Recognition
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2023
Source: Laboratories of Jonas T. Kaplan and Sarah I. Gimbel—University of Southern California The autonomic nervous system (ANS) controls the activity of the body's internal organs and regulates changes in their activity depending on the current environment. The vagus nerve, which innervates many of the internal organs, is an important part of the system. When our brain senses danger, vagal tone is inhibited, leading to a set of changes in the body designed to make us more prepared to fight or...
FAQs
Fundamental frequency provides a measurable representation of vocal pitch, while pitch movement captures how that pitch changes across a spoken syllable. Duration and intensity add temporal and energy-related information that can help separate patterns that are acoustically similar. Examining these features together gives tone recognition systems a broader basis for classifying tone in context.
The surrounding speech context can affect how a tone is realized and how its acoustic pattern should be interpreted. For that reason, classification should consider the spoken syllable in context rather than treating every pitch pattern as isolated. Context-sensitive analysis can support more reliable recognition when pitch movement, duration, or intensity varies across utterances.
Signal-processing methods analyze acoustic measurements such as fundamental frequency, pitch movement, duration, and intensity to characterize a syllable. Machine-learning methods use those measurements to classify the associated tone. The two approaches therefore represent complementary stages: feature analysis describes the speech signal, while classification assigns an interpreted tone pattern based on the available acoustic information.
Recognition depends on how clearly the speech signal expresses pitch movement and related acoustic cues. Changes in pitch control, duration, intensity, or the surrounding context may alter the measurable pattern used for classification. In medical assessment, these variables are especially relevant because neurological injury or communication disorders can produce changes in pitch control and prosody.
A typical workflow begins with examining a spoken syllable and extracting acoustic features, including fundamental frequency, pitch movement, duration, and intensity. Signal-processing or machine-learning methods then classify the tone associated with that syllable in context. The resulting analysis can characterize tone patterns and provide structured information for speech-language assessment or language technology.
In medicine, tone analysis can help characterize changes in pitch control and prosody after neurological injury or in communication disorders. It may contribute to speech-language assessment by documenting acoustic patterns in a structured way, rather than relying only on general listening impressions. The results can support clinical documentation and computer-assisted assessment when interpreted within the broader speech context.