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相关概念视频

Types Of Transformers01:16

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Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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The Ideal Transformer01:26

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In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Transformers01:26

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A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
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Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now? 
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相关实验视频

Updated: Sep 12, 2025

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

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基于变压器的ASR系统中的自适应性上下文偏差.

Nurmemet Yolwas1, Yineng Cai2, Lixu Sun2

  • 1College of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China. nurmemet@xju.edu.cn.

Scientific reports
|August 6, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了用于自动语音识别 (ASR) 的上下文偏差适应模型. 适应模型通过仅在检测到有偏见的单词时应用上下文偏差来降低计算成本,从而提高效率.

关键词:
情境偏见是指情境上的偏见.个性化 个性化语音识别 语音识别 语音识别

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相关实验视频

Last Updated: Sep 12, 2025

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

  • 人工智能的人工智能
  • 计算机科学 计算机科学
  • 计算语言学 计算语言学

背景情况:

  • 端到端的神经自动语音识别 (ASR) 系统擅长识别上下文偏见的单词.
  • 当前的ASR系统面临着偏差层的挑战,包括增加的计算复杂性,资源需求和冗余偏差.

研究的目的:

  • 为ASR系统提出一个新的上下文偏差适应模型.
  • 仅在输入音频中存在有偏见的单词时,才能动态评估和应用上下文偏差.
  • 为了减轻上下文偏差的负面影响,同时减少计算开销.

主要方法:

  • 开发一个适应上下文偏差模型.
  • 在输入音频中动态评估偏见的单词.
  • 基于检测到的偏见词的偏见层的条件激活.

主要成果:

  • 背景偏差适应模型有效地减轻了上下文偏差的不利影响.
  • 与传统的ASR系统相比,计算成本大幅降低.
  • 偏差层的激活是优化的,只有在必要时才会发生.

结论:

  • 拟议的自适应模型提供了一种有效的方法来处理ASR中的上下文偏差.
  • 这种方法平衡了对上下文准确性的需求与计算资源管理的需求.
  • 适应性战略在优化ASR系统性能和效率方面取得了重大进展.