ArabicSpeech – A community for all Arabic speakers to enhance arabic speech research

— Speech Technologies

Technologies behind Speech analysis

— Arabic Speech Resources

Enhance Arabic dialect speech resources

— Meetups

Annual meetings

About

ArabicSpeech is a community that runs for the benefit of Arabic Speech Science and Speech Technologies. Arabic speech is concerned with technologies in speech and language processing. Our main focus will be the Arabic language, both standard and dialectal.

ArabicSpeech will help to build innovation and technology capacities for the Arabic language. It will focus on tackling large-scale computing challenges that address real priorities that will impact people’s life. We address the state of the art as well as the state of the market challenges. We help to push the boundaries for Arabic speech to make it world-class technology.

Automatic Speech Recognition

Automatic Speech Recognition (ASR) is the process of converting the speech signal into its corresponding text. The quality of ASR systems is measured by how close their recognized sequences of words are to human recognized sequences of words.

Arabic Dialect Identifcation

Dialect identification (DID) is a special case of Language identification (LID) problem. This is the process of automatically identifying the language class for a given speech segment.

Text to Speech

The Text to Speech (TTS) technology aims to convert a sequence of words into speech.

Language Models

Language Models are building block of many speech and NLP technology that aims at accurately estimating the probability distribution of a word sequences or sentences produced in a natural language

Our events

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Events for July

31st

No Events

Arabic SpeechFollow

Retweet on Twitter Arabic Speech Retweeted

Kanari AI @kanari_ai·

19 Jul

We are excited to share our new speech recognition milestone:
Multi-lingual, multi-dialectal Arabic speech recognition.
Joint research with @qatarcomputing @hbku
Coming soon at @interspeech2021

@arabicspeech

https://vimeo.com/576852871/932f1351c0

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Michael Auli @MichaelAuli·

21 May

Today we are announcing our work on building speech recognition models without any labeled data! wav2vec-U rivals some of the best supervised systems from only two years ago.

Paper: https://ai.facebook.com/research/publications/unsupervised-speech-recognition
Blog: https://ai.facebook.com/blog/wav2vec-unsupervised-speech-recognition-without-supervision
Code: https://github.com/pytorch/fairseq/tree/master/examples/wav2vec/unsupervised

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Yann LeCun @ylecun·

21 May

Wave2vec-Unsupervised is particularly useful for languages, dialects, and accents for which little or no transcribed data is available.
Here is an example demo in Swahili. https://twitter.com/MichaelAuli/status/1395774305372237825

Michael Auli@MichaelAuli

It also works in languages other than English, see the Swahili demo below. So far we tried it on Kyrgyz, Tatar, German, Dutch, French, Spanish, Portuguese, Italian.

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SpeechBrain @SpeechBrain1·

15 Mar

Thanks, @mircoravanelli!
I hope I can be helpful to all the developers of #speech technologies that are looking for a simple and intuitive #opensource toolkit. I still support many #speech tasks and, with a bigger community, I can keep growing!

https://speechbrain.github.io/ https://twitter.com/mirco_ravanelli/status/1371464761536348161

Mirco Ravanelli@mirco_ravanelli

I'm thrilled to announce the public release of #SpeechBrain!

#SpeechBrain is an #opensource toolkit designed to make research and development of #speech technologies faster. It is flexible, modular, easy-to-use, well documented.

https://speechbrain.github.io/

#DeepLearning #AI

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🔥 Kareem Carr 🔥@kareem_carr·

27 Mar

FOUR things to know about race and gender bias in algorithms:

1. The bias starts in the data

2. The algorithms don't create the bias but they do transmit it

3. There are a huge number of other biases. Race and gender bias are just the most obvious

4. It's fixable! 🧵👇

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