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Articles related to "speech"


Facebook and Twitter have similar policies. But only Twitter is fighting Trump.

  • Twitter’s decision to fact-check President Donald Trump on Tuesday around misleading statements about voting by mail has been followed by an unprecedented escalation of Trump’s war on social media.
  • Despite having very similar policies around content moderation to Twitter, Facebook hasn’t applied any of its standard fact-checking labels to Trump’s posts (identical to the recent ones on Twitter), and it doesn’t plan to.
  • Zuckerberg did briefly critique Trump’s push to regulate social media’s content moderation, saying that “government choosing to censor a platform because they’re worried about censorship doesn’t exactly strike me as the right reflex.” But the resounding message of his interview was that Twitter was in this battle alone.
  • Google has stayed noticeably silent on Twitter’s battle over fact-checking Trump, although the company regularly deals with similar issues around moderating political content because of its video-sharing platform, YouTube.

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Former Facebook employees forcefully join the chorus against Mark Zuckerberg

  • The latest salvo comes from 33 former employees who worked at the company during its early stages, including some who helped create Facebook’s original community guidelines.
  • Facebook’s leadership must reconsider their policies regarding political speech, beginning by fact-checking politicians and explicitly labeling harmful posts.
  • As early employees on teams across the company, we authored the original Community Standards, contributed code to products that gave voice to people and public figures, and helped to create a company culture around connection and freedom of expression.
  • Facebook already is acting, as Mark Zuckerberg put it on Friday, as the “arbiter of truth.” It monitors speech all the time when it adds warnings to links, downranks content to reduce its spread, and fact checks political speech from non-politicians.
  • Thanks to work done by the Dangerous Speech Project and many others, we understand the power words have to increase the likelihood of violence.

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Some popular Reddit communities go private to protest the platform’s hate speech policies

  • Some of Reddit’s most frequented and active subreddit communities are participating in a collective action this week to protest the platform’s hate speech policies and the current police brutality and racism crisis gripping the US.
  • The decision follows harsh criticism from former CEO Ellen Pao, who said on Twitter earlier this week that the company “monetizes white supremacy and hate all day long.” She also said Reddit should have long ago banned r/The_Donald, the controversial pro-Trump subreddit it quarantined last year for repeatedly violating its rules around hate speech and violent rhetoric.
  • Protest actions include moderators taking popular subreddits private and banning the publishing of new posts for either a full day or for eight minutes and 46 seconds in honor of George Floyd, the Minnesota black man who was killed by a former Minneapolis police officer last week.

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Zoom explains why free users won't get encrypted video calls

  • Last week, the company’s security consultant Alex Stamos revealed that plans for tightened security on paying accounts were in the pipeline — today Zoom CEO Eric Yuan has confirmed it.
  • According to Stamos, Zoom faces a “difficult balancing act” trying to improve privacy guarantees while “reducing the human impact of the abuse of its product.” Here, he’s referring to hate speech, exposure to children and other illegal behaviors which have blighted Zoom in recent times.
  • Those involved in this type of activity will mostly use a free account with throwaway email addresses – a lower level of encryption will allow Zoom, with the assistance of law enforcement, to take action on repeat offenders.
  • And now, as the platform is increasingly being used by nefarious individuals for illegal activities, Zoom — like all other tech companies — must strike a balance between security for its trusted users, and mechanisms for weeding out the bad actors.

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Nine things we learned from leaked audio of Mark Zuckerberg facing his employees

  • On Tuesday morning, Mark Zuckerberg held a meeting with employees over video chat to address concerns related to the company’s decision not to take action on some recent posts by President Trump.
  • from what our policy is and the principles of the platform we’re running are — knowing that the decision that we made was going to lead to a lot of people being very upset inside the company and a lot of the media criticism we’re going to get,” said Zuckerberg.
  • The optimists argue that social media is neither good nor bad, but simply a powerful new tool for society to use.
  • This year might be a particularly fraught moment, the optimists say, but eventually (and maybe even soon!), things will start getting back to normal and we’ll be glad we preserved our free speech traditions so that movements like Black Lives Matter can continue to use these tools for positive ends.

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Papers with Code

  • By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do.
  • We present a new method that views object detection as a direct set prediction problem.
  • Human motion is fundamental to understanding behavior.
  • In this work, we explore the task of lip to speech synthesis, i.
  • e., learning to generate natural speech given only the lip movements of a speaker.
  • In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations.
  • To achieve this, we decouple appearance and motion information using a self-supervised formulation.
  • Loss functions are one of the crucial ingredients in deep learning-based medical image segmentation methods.

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Facebook employees are fed up with how Mark Zuckerberg is handling Trump’s “looting ... shooting” post

  • After Trump posted on Friday on both Facebook and Twitter that “when the looting starts, the shooting starts” in response to protests in Minneapolis, civil rights leaders from groups such as Color of Change — as well some of Facebook’s own employees — called for the social media companies to take the posts down or otherwise flag them for violent rhetoric.
  • Twitter left up the controversial post but put a warning label on it, building on a decision it made earlier in the week when it fact-checked the president’s tweets that shared misleading information about voting by mail.
  • In addition to voicing their anger, many Facebook employees proposed solutions to Facebook’s issues around moderating Trump, such as reworking its hate speech rules or applying its policies around violent speech and misleading information to politicians as stringently as it does to regular people.

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Indian Accent Speech Recognition

  • We can think of a human voice production model as a combination of source and filter, where the source is unique to an individual and the filter is the articulation of words that we all use when speaking.
  • We could train an HMM with labelled time series sequences to create individual HMM models for each particular sound unit.
  • Lets plot above metrics, feeding Indian Accent Speech Data (Test Set) to both DeepSpeech pre-trained model and our trained model to compare.
  • The above depiction proves that the trained model performs much better for Indian Accent Speech Recognition compared to DeepSpeech model.
  • We have seen ‘Cepstral Analysis’ separate out the accent components in speech signals, while doing Feature Extraction (MFCC) in Traditional ASR.
  • We have proved the case, by doing transfer learning Baidu’s DeepSpeech pre-trained model on Indian-English Speech data from multiple states.

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Indian Accent Speech Recognition

  • We can think of a human voice production model as a combination of source and filter, where the source is unique to an individual and the filter is the articulation of words that we all use when speaking.
  • We could train an HMM with labelled time series sequences to create individual HMM models for each particular sound unit.
  • Lets plot above metrics, feeding Indian Accent Speech Data (Test Set) to both DeepSpeech pre-trained model and our trained model to compare.
  • The above depiction proves that the trained model performs much better for Indian Accent Speech Recognition compared to DeepSpeech model.
  • We have seen ‘Cepstral Analysis’ separate out the accent components in speech signals, while doing Feature Extraction (MFCC) in Traditional ASR.
  • We have proved the case, by doing transfer learning Baidu’s DeepSpeech pre-trained model on Indian-English Speech data from multiple states.

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