https://store-images.s-microsoft.com/image/apps.62189.94dc5b61-3a7c-499b-927e-34c86808e83c.e26d8ca7-8392-4d6f-99bb-448d7ee6b5f7.f6113bba-efe4-49f5-8860-3cb3e7523e58
Arabic Speech to Text (STT)
NeuralSpace
Arabic Speech to Text (STT)
NeuralSpace
Arabic Speech to Text (STT)
NeuralSpace
The most accurate ASR transcription for Arabic dialects
Automatically transcribe Speech to Text in Arabic dialects. NeuralSpace Speech to Text (STT), or Automatic Speech Recognition (ASR), converts audio into text automatically, and on any scale.
The NeuralSpace Speech-to-Text transcription models are the most accurate on the market for Arabic dialects. NeuralSpace's AI models are trained on a wide range of Arabic datasets which enable the service to support over 10 Arabic dialects including: Saudi, Gulf, Jordanian, Egyptian, Moroccan, Lebanese, Omani, Qatari, Tunisian, Kuwaiti, Iraqi.
Check out the accuracy benchmarking we recently did against other providers, please see this report:
https://www.neuralspace.ai/arabic-speech-to-text-comparing-results-of-top-stt-providers
Capabilities:
- Deep learning-based Automatic Speech Recognition (STT, Speech-To-Text)
- Speaker identification
- Background noise cancelation
- Able to understand several languages in one file (code-mixed transcription)
- Batch file or live transcription
Full speech support with on-demand customisations and on-premise deployment
- Easy integration with other systems
About NeuralSpace:
NeuralSpace is a Natural Language Processing company. Our mission is to break down the language barrier encountered worldwide by people whose first language is not English by enabling access to cutting-edge language AI for locally spoken languages.
Book a demo or contact: sales@neuralspace.ai
Docs pages: https://docs.neuralspace.ai/speech-to-text/overview
https://store-images.s-microsoft.com/image/apps.32283.94dc5b61-3a7c-499b-927e-34c86808e83c.e26d8ca7-8392-4d6f-99bb-448d7ee6b5f7.7b4ff93d-b587-421e-8196-efa0adffe285
https://store-images.s-microsoft.com/image/apps.32283.94dc5b61-3a7c-499b-927e-34c86808e83c.e26d8ca7-8392-4d6f-99bb-448d7ee6b5f7.7b4ff93d-b587-421e-8196-efa0adffe285