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Language Development01:22

Language Development

498
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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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 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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Automatic Processing and Automatic Social Behavior01:28

Automatic Processing and Automatic Social Behavior

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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
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Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

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Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
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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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Related Experiment Video

Updated: Oct 1, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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RNN Language Processing Model-Driven Spoken Dialogue System Modeling Method.

Xia Zhu1

  • 1Foreign Language Department, Guangzhou Huashang College, Guangdong 511300, China.

Computational Intelligence and Neuroscience
|March 8, 2022
PubMed
Summary
This summary is machine-generated.

This study improves spoken language understanding (SLU) in spoken dialogue systems (SDS) by using Recurrent Neural Network (RNN) language models to rescore recognition results. This method enhances accuracy, especially when test data differs from training data.

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Speech Technology

Background:

  • Spoken dialogue systems (SDS) rely heavily on accurate speech recognition and semantic understanding for effective performance.
  • Improving spoken language understanding (SLU) is crucial for advancing SDS capabilities.

Purpose of the Study:

  • To enhance the accuracy of SLU in SDS by optimizing the language model's performance.
  • To address the challenge of data mismatch between training and testing sets in speech recognition.

Main Methods:

  • Utilized Recurrent Neural Network (RNN) language models to predict text sequences.
  • Introduced RNN language model probability scores to rescore intermediate recognition results.
  • Proposed a cache RNN model combination to optimize decoding and improve word sequence probability calculations.

Main Results:

  • The proposed method significantly improved the performance of the speech recognition system on test data.
  • Demonstrated the potential for achieving higher SLU scores through the implemented approach.

Conclusions:

  • The developed method effectively enhances speech recognition accuracy, particularly for unseen data.
  • This research offers valuable insights for future advancements in spoken dialogue systems and SLU.