Method for scoring in an automatic speech recognition system
AssigneeNuance Communications, Inc.
InventorsJoel Pinto, Daniel Willett, Christian Plahl
Life of this patent
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Abstract
A system and method for speech recognition is provided. Embodiments may include receiving an audio signal at a first deep neural network (“DNN”) associated with a computing device. Embodiments may further include receiving the audio signal at a second deep neural network (“DNN”) associated with a computing device, wherein the second deep neural network includes fewer parameters than the first deep neural network. Embodiments may also include determining whether to select an output from the first deep neural network or the second deep neural network and providing the selected output to a decoder with an overall objective of speeding up ASR.
Claims18
A computer-implemented method comprising:
- receiving an audio signal at a first deep neural network associated with a computing device;
- receiving the audio signal at a second deep neural network associated with the computing device, wherein the second deep neural network includes fewer parameters than the first deep neural network;
- determining one or more phonetic class conditional probabilities of the audio signal, wherein the one or more phonetic class conditional probabilities include at least one of silence, vowels, and consonants;
- determining whether to select an output from one of the first deep neural network and the second deep neural network based upon, at least in part, the one or more phonetic class conditional probabilities of the audio signal;
- and
- selecting the output from one of the first deep neural network and the second deep neural network based upon, at least in part, the one or more phonetic class conditional probabilities of the audio signal.
A system comprising:
- one or more processors configured to receive an audio signal at a first deep neural network associated with a computing device, the one or more processors further configured to receive the audio signal at a second deep neural network associated with a computing device, wherein the second deep neural network includes fewer parameters than the first deep neural network, the one or more processors further configured to determine one or more phonetic class conditional probabilities of the audio signal, wherein the one or more phonetic class conditional probabilities include at least one of silence, vowels, and consonants, the one or more processors further configured to determine whether to select an output from one of the first deep neural network and the second deep neural network based upon, at least in part, the one or more phonetic class conditional probabilities of the audio signal, the one or more processors further configured to select the output from one of the first deep neural network and the second deep neural network based upon, at least in part, the one or more phonetic class conditional probabilities of the audio signal.
Dependencies are inferred from the claim text.