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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September 2021

October

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Arabic SpeechFollow

Retweet on Twitter Arabic Speech Retweeted

Yann LeCun @ylecun·

10 Sep

There is lot more to natural languages than text: tone, accent, expression, prosody, timbre, pitch.....
"Textless NLP" represents speech through a stream of discrete tokens, automatically learned through self-supervised learning, directly fed with raw speech waveform!
A new era. https://twitter.com/facebookai/status/1436028477895942155

Facebook AI@facebookai

We’re introducing GSLM, the first language model that breaks free completely of the dependence on text for training. This “textless NLP” approach learns to generate expressive speech using only raw audio recordings as input. Learn more and get the code:
https://ai.facebook.com/blog/textless-nlp-generating-expressive-speech-from-raw-audio

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Arabic Speech @ArabicSpeech·

6 Sep

Glad to see many people enjoyed attending in person.

Interspeech 2021@INTERSPEECH2021

Missed the beautiful summary video of me during the closing ceremony? Want to relive the past amazing week? Want to know what you missed?

Check out the video here:
https://youtu.be/onsGuNum5Gs

🌟❤️🌟

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Arabic Speech @ArabicSpeech·

3 Sep

Interesting takeaway lesson from LM & AI research by Tomas Mikolov @INTERSPEECH2021

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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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Retweet on Twitter Arabic Speech Retweeted

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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