Speech Recognition or Automatic Speech Recognition (ASR) is the automatic conversion of Speech into Text. In technical terms, it is the process of converting a spoken utterance into a sequence of word hypothesis that is closer to the original transcription. Current state of the art ASR system has outperformed conventional ASR system.

The performance of deep neural networks in ASR has reached to professional human transcribers in clean speech environment conditions. However, it has been affected by the following challenges:

• Physical and social variances of speakers.
• Environmental and channel distortions.
• Room reverberation in far-field ASR.
• Code-switched phenomena.
• The mismatch between training and test data.
• Children speech recognition
• Aged people speech recognition

Our ASR model is based on state of the art deep neural networks.

Our ASR model is robust enough that it can handle the above challenges in a professional way. Moreover; our ASR model can be adapted to specific domain in order to acheive higher accuracy.

It can transcribe noisy audio recordings, code-switched recordings, variety of speaker's recordings etc. It can be used for generating transcription of audio or video, Telephonic conversation, Podcasts transcriptions, virtual meeting transcriptions and video/Audio lecture transcriptions.