16-06-2016 дата публикации
Номер: US20160171974A1
Автор:
Case Carl,
Casper Jared,
Catanzaro Bryan,
COATES Adam,
DIAMOS Gregory,
Elsen Erich,
Hannun Awni,
Ng Andrew Y.,
Prenger Ryan,
SATHEESH Sanjeev,
Sengupta Shubhabrata
Принадлежит:
Baidu USA LLC
Presented herein are embodiments of state-of-the-art speech recognition systems developed using end-to-end deep learning. In embodiments, the model architecture is significantly simpler than traditional speech systems, which rely on laboriously engineered processing pipelines; these traditional systems also tend to perform poorly when used in noisy environments. In contrast, embodiments of the system do not need hand-designed components to model background noise, reverberation, or speaker variation, but instead directly learn a function that is robust to such effects. A phoneme dictionary, nor even the concept of a “phoneme,” is needed. Embodiments include a well-optimized recurrent neural network (RNN) training system that can use multiple GPUs, as well as a set of novel data synthesis techniques that allows for a large amount of varied data for training to be efficiently obtained. Embodiments of the system can also handle challenging noisy environments better than widely used, state-of-the-art commercial speech systems. 1. A computer-implemented method for training a transcription model , the method comprising: inputting an utterance that comprises a set of spectrogram frames into a first layer of the transcription model that evaluates each of the spectrogram frames from the set of spectrogram frames with a context of one or more spectrogram frames;', 'outputting from the transcription model a predicted character or character probabilities for the utterance; and', 'computing a loss to measure error in prediction for the utterance;, 'for each of a set of utterancesevaluating a gradient of predicted outputs of the transcription model given the ground-truth characters; andupdating the neural network model using back-propagation.2. The computer-implemented method of further comprising:jittering at least some of the set of utterances prior to inputting into the transcription model.3. The computer-implemented method of wherein the step of jittering at least some of the ...
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