US 10,540,957 B2 · Systems and methods for speech transcription

Abstract

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.

Claims20

Dependent claims

A computer-implemented method for transcribing speech comprising:

receiving an input audio from a user;
normalizing the input audio to make a total power of the input audio consistent with a set of training samples used to train a trained neural network;
generating a jitter set of audio files from the normalized input audio by translating the normalized input audio by one or more time values;
for each audio file from the jitter set of audio files, which includes the normalized input audio:
generating a set of spectrogram frames for each audio file;
inputting the set of spectrogram frames into a trained neural network;
obtaining predicted character probabilities outputs from the trained neural network;
and
decoding a transcription of the input audio using the predicted character probabilities outputs from the trained neural network constrained by a language model that interprets a string of characters from the predicted character probabilities outputs as a word or words.

6 more elements

A non-transitory computer-readable medium or media comprising one or more sequences of instructions which, when executed by one or more processors, causes the steps to be performed comprising:

receiving an input audio from a user;
generating a set of spectrogram frames from the input audio;
inputting the set of spectrogram frames into a set of trained neural networks;
obtaining predicted character probabilities outputs from the set of trained neural networks;
and
decoding a transcription of the input audio using the predicted character probabilities outputs from the set of trained neural networks constrained by a language model that interprets a string of characters from the predicted character probabilities outputs as a word or words.

3 more elements

Dependencies are inferred from the claim text.

Systems and methods for speech transcription

Life of this patent

Systems and methods for speech transcription

Life of this patent

Abstract

Claims20