2 madurai polarities from forma will net you enough to add Primed Target cracker, Hemorrhage, and Creeping Bullseye to Euphona Prime, because the firerate becomes 2.10, thus giving you 70% chance to proc slash from the weapon's high impact damage stat.
Millions of people suffer from speech problems, which can be caused by anything including neurological or genetic diseases, physical handicaps, brain damage, or hearing loss. Speech patterns such as stuttering, dysarthria, apraxia, and others that result from speech disorders make it difficult for people to express themselves and access voice-enabled devices.
With the increased computational power of deep learning systems and the availability of large training datasets, the accuracy of automated speech recognition (ASR) technologies has improved. However, their performance is still unsatisfactory for many people with speech difficulties, rendering the technology unsuitable for many speakers who could benefit from it.
The Google AI team recently released the findings of their research, which intended to make personalized ASR models accessible to more people. To accomplish this, the researchers expanded the existing disordered speech data and focused on generating personalized ASR models based on this corpus. Compared to out-of-the-box speech models trained on ordinary speech, this approach produces highly accurate models that can improve the word mistake rate by up to 85% in some domains.
Audio: https://www.youtube.com/watch?v=sokmD36rDnk
Research (Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia): https://www.isca-speech.org/archive/interspeech_2021/macdonald21_interspeech.html
Research (Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases): https://www.isca-speech.org/archive/interspeech_2021/green21_interspeech.html
Google Blog: https://ai.googleblog.com/2021/09/personalized-asr-models-from-large-and.html
https://pr
... keep reading on reddit ➡
The approach is centered on analyzing speech recordings to better train speech recognition models. Give a sample of your atypical speech as you read a few paragraphs, deadline is Feb 5th, https://sites.research.google/euphonia/about/
Atypical can refer to developmental language disorder, stutter, lisps and much more.
Dear redditors of r/ALS,
Some of you may already came across a project of Google called "Project Euphonia". For those who don't know it yet, here's a short description:"Project Euphonia is a Google Research initiative focused on helping people with atypical speech to be better understood. The approach is centered on analyzing speech recordings to better train speech recognition models."
Speech rocognition can also be very frustrating for people diagnosed with ALS. But those people can help improving it by recording sentences for the artificial intelligence to get trained.
For more information about Project Euphonia and how to participate visit Google's research page.
Hi all,
I recently came across these two projects
https://sites.google.com/view/project-euphonia/
Project Euphonia is run by Google and Voiceitt is a separate organisation which has been around for a few years. They're both trying to achieve similar goals. To make a voice recognition system which can understand people with speech impairments.
I did a quick search of this subreddit and was surprised to see neither had been mentioned. I would really recommend checking out the link for project euphonia as they're still looking for people to give speech samples and you may have a client who can help out.
Relax and check out the moonlight on the ocean! Visit my town frequently to watch it develop into the ideal image I see! Feel free to use any of my patterns. :)
Millions of people suffer from speech problems, which can be caused by anything including neurological or genetic diseases, physical handicaps, brain damage, or hearing loss. Speech patterns such as stuttering, dysarthria, apraxia, and others that result from speech disorders make it difficult for people to express themselves and access voice-enabled devices.
With the increased computational power of deep learning systems and the availability of large training datasets, the accuracy of automated speech recognition (ASR) technologies has improved. However, their performance is still unsatisfactory for many people with speech difficulties, rendering the technology unsuitable for many speakers who could benefit from it.
The Google AI team recently released the findings of their research, which intended to make personalized ASR models accessible to more people. To accomplish this, the researchers expanded the existing disordered speech data and focused on generating personalized ASR models based on this corpus. Compared to out-of-the-box speech models trained on ordinary speech, this approach produces highly accurate models that can improve the word mistake rate by up to 85% in some domains.
Audio: https://www.youtube.com/watch?v=sokmD36rDnk
Research (Disordered Speech Data Collection: Lessons Learned at 1 Million Utterances from Project Euphonia): https://www.isca-speech.org/archive/interspeech_2021/macdonald21_interspeech.html
Research (Automatic Speech Recognition of Disordered Speech: Personalized models outperforming human listeners on short phrases): https://www.isca-speech.org/archive/interspeech_2021/green21_interspeech.html
Google Blog: https://ai.googleblog.com/2021/09/personalized-asr-models-from-large-and.html
https://pr
... keep reading on reddit ➡