speech machines.org

The Internet As a Speech Machine and Other Myths Confounding …

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3532691

The Internet As a Speech Machine and Other Myths Confounding ...
The Internet As a Speech Machine and Other Myths Confounding …

Feb 7, 2020 … Danielle Keats Citron · Mary Anne Franks · (explaining that “common law has not had a meaningful hand in shaping intermediaries’ moderation of …

A Wireless Brain-Machine Interface for Real-Time Speech Synthesis …

http://t.umblr.com/redirect?z=http%3A%2F%2Fjournals.plos.org%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0008218&t=OTI5NDZkNzlmNzIyZDk5OWVjZjUxMzRjMzE4ZDZiOWM3OTc1NjBiMCxLbmRMQ3BzVA%3D%3D&b=t%3Ar89k9zEEixswzlcCIu47sQ&p=https%3A%2F%2Fjim1701.tumblr.com%2Fpost%2F167704691015%2Fdirect-communication-from-brain-to-computer&m=1

Dec 9, 2009 … Background Brain-machine interfaces (BMIs) involving electrodes … PLoS ONE 4(12): e8218. https://doi.org/10.1371/journal.pone.0008218.

The Speech Recognition Virtual Kitchen · GitHub

https://github.com/srvk

Virtual Machines and Containers for Automatic Speech Recognition Research, Development, and Education – The Speech Recognition Virtual Kitchen

Welcome to Speechmachines.org

http://data.danetsoft.com/speechmachines.org

Speechmachines.org is a malware-free website without age restrictions, so you can safely browse it. It seems that Speechmachines team are just starting to …

Mistaking minds and machines: How speech affects dehumanization …

https://feedproxy.google.com/~r/apa-journals-ofp-xge/~3/nkAhexpgHgU/

Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person’s actual mental experience—a humanlike voice—affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text’s creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text’s creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual c

Machine learning for decoding listeners’ attention from …

https://onlinelibrary.wiley.com/doi/10.1111/ejn.13790

Neuronal networks achieved higher performance than linear regression in decoding auditory attention by reconstructing the attended envelope from high-density scalp electroencephalography. Analysis of…

Future Speech Interfaces with Sensors and Machine Intelligence

https://www.mdpi.com/1424-8220/23/4/1971

Author to whom correspondence should be addressed. Sensors 2023, 23(4), 1971; https://doi.org/10.3390/s23041971. Received: 2 …

Conformer: Convolution-augmented Transformer for Speech …

https://telegram-site.com/site/outurl?link=https://arxiv.org/abs/2005.08100

Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer models are good at capturing content-based global interactions, while CNNs exploit local features effectively. In this work, we achieve the best of both worlds by studying how to combine convolution neural networks and transformers to model both local and global dependencies of an audio sequence in a parameter-efficient way. To this regard, we propose the convolution-augmented transformer for speech recognition, named Conformer. Conformer significantly outperforms the previous Transformer and CNN based models achieving state-of-the-art accuracies. On the widely used LibriSpeech benchmark, our model achieves WER of 2.1%/4.3% without using a language model and 1.9%/3.9% with an external language model on test/testother. We also observe competitive performance of 2.7%/6.3% with a small model of only 10M parameters.

Cyber Hate Speech on Twitter: An Application of Machine …

https://onlinelibrary.wiley.com/doi/full/10.1002/poi3.85

The use of “Big Data” in policy and decision making is a current topic of debate. The 2013 murder of Drummer Lee Rigby in Woolwich, London, UK led to an extensive public reaction on social media, pr…

MLCommons

http://www.mlcommons.com/

MLCommons aims to accelerate machine learning innovation to benefit everyone.

Similar Posts