AUTOMATED SPEECH RECOGNITION APPROACHES AND CHALLENGES

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  • examples

    TensorFlow examples (by tensorflow)

    The goal of this approach is to replace the intermediate steps with one algorithm. The deep learning approach has achieved state-of-the-art results in speech transcription tasks and is replacing the traditional methods used in ASR. It is also simpler because there are fewer steps involved and does not require as much expertise. The implementation of this approach requires a knowledge understanding of deep learning tools such as PyTorch, Tensorflow, DeepSpeech, etc.

  • Pytorch

    Tensors and Dynamic neural networks in Python with strong GPU acceleration

    The goal of this approach is to replace the intermediate steps with one algorithm. The deep learning approach has achieved state-of-the-art results in speech transcription tasks and is replacing the traditional methods used in ASR. It is also simpler because there are fewer steps involved and does not require as much expertise. The implementation of this approach requires a knowledge understanding of deep learning tools such as PyTorch, Tensorflow, DeepSpeech, etc.

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

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