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Related Concept Videos

Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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Language Development01:22

Language Development

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
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Language and Cognition01:27

Language and Cognition

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Language serves as a bridge between ideas and communication, influencing how individuals perceive and interact with the world. Psychologists have long debated whether language shapes thought or vice versa. This discussion gained grip with Edward Sapir and Benjamin Lee Whorf in the 1940s, who proposed that language determines thought, a concept known as linguistic determinism. They suggested that the vocabulary and structure of a language influence how its speakers think and perceive reality.
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Elaborative Rehearsals01:07

Elaborative Rehearsals

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Elaborative rehearsal is a crucial cognitive strategy that strengthens information encoding in long-term memory by making meaningful connections between new data and pre-existing knowledge. This approach contrasts with maintenance rehearsal, which involves simple repetition without delving into the significance of the information. While maintenance rehearsal might temporarily keep information active in short-term memory, it is less effective for long-term retention.
The effectiveness of...
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Sound Waves: Resonance01:14

Sound Waves: Resonance

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Resonance is produced depending on the boundary conditions imposed on a wave. Resonance can be produced in a string under tension with symmetrical boundary conditions (i.e., has a node at each end). A node is defined as a fixed point where the string does not move. The symmetrical boundary conditions result in some frequencies resonating and producing standing waves, while other frequencies interfere destructively. Sound waves can resonate in a hollow tube, and the frequencies of the sound...
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Perceiving Loudness, Pitch, and Location01:21

Perceiving Loudness, Pitch, and Location

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The human brain perceives pitch through two primary mechanisms reflected in place theory and frequency theory. Each mechanism describes how sound waves are interpreted as specific pitches by the brain, offering insights into the intricate processes of auditory perception.
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Related Experiment Video

Updated: Jul 23, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences.

Jeremy E Matt1, Donna M Rizzo2, Ali Javed3

  • 1Graduate Program in Complex Systems and Data Science, College of Engineering and Mathematical Sciences, University of Vermont, Burlington, Vermont, USA.

Journal of Palliative Medicine
|July 13, 2023
PubMed
Summary
This summary is machine-generated.

Automated machine learning accurately identifies "Connectional Silence" in patient conversations, improving healthcare communication analytics and quality. This method enhances understanding of patient outcomes through analyzing speech patterns.

Keywords:
artificial intelligenceconversation analysishuman connectionmachine-learningsilence

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Area of Science:

  • Healthcare Communication Science
  • Quality Improvement
  • Computational Linguistics

Background:

  • Scalable conversation analytics are crucial for advancing healthcare communication science.
  • Identifying conversational features like "Connectional Silence" is vital for improving patient outcomes.
  • Existing methods for analyzing clinical conversations are often manual and time-consuming.

Purpose of the Study:

  • To evaluate the feasibility of automating the detection of "Connectional Silence" in clinical conversations.
  • To develop and validate a machine learning pipeline for identifying this specific conversational feature.
  • To assess the performance of automated methods in recognizing "Connectional Silence" and its subtypes.

Main Methods:

  • Utilized audio recordings from the Palliative Care Communication Research Initiative cohort study.
  • Developed a three-stage machine learning pipeline: random forest, convolutional neural network, and natural language processing.
  • Employed audio analysis and automated speech-to-text transcripts for feature identification.

Main Results:

  • The machine learning pipeline achieved 84% sensitivity and 92% specificity in identifying "Connectional Silence".
  • Specific subtypes, "Emotional" and "Invitational" Connectional Silence, were detected with 68% and 67% sensitivity, and 95% and 97% specificity, respectively.
  • Demonstrated the effectiveness of coordinated machine learning tools in automating the analysis of clinical dialogue.

Conclusions:

  • Coordinated and complementary machine learning methods can fully automate the identification of "Connectional Silence" in real-world clinical settings.
  • Automated analysis of "Connectional Silence" holds significant potential for healthcare communication research and quality improvement.
  • This approach offers a scalable solution for analyzing nuanced aspects of patient-provider interactions.