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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

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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

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

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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