EFFICIENT GENERATION OF COMPLEMENTARY ACOUSTIC MODELS FOR PERFORMING AUTOMATIC SPEECH RECOGNITION SYSTEM COMBINATION

• US 20160034811A1
• Filed: 09/30/2014
• Published: 02/04/2016
• Est. Priority Date: 07/31/2014
• Status: Abandoned Application

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: PAULIK, Matthias, KRISHNAMOORTHY, Mahesh
• Application Number:
  US14/503,028
• Publication Number:
  US 20160034811A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 3/045 Combinations of networks
• CPC Class Codes: G06N 3/047 Probabilistic or stochastic networks
• CPC Class Codes: G10L 15/16 using artificial neural networks

Abstract

Systems and processes for generating complementary acoustic models for performing automatic speech recognition system combination are provided. In one example process, a deep neural network can be trained using a set of training data. The trained deep neural network can be a deep neural network acoustic model. A Gaussian-mixture model can be linked to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model. The Gaussian-mixture model can be trained via a first portion of the trained deep neural network and using the set of training data. The first portion of the trained deep neural network can include an input layer of the deep neural network and the hidden layer. The first portion of the trained deep neural network and the trained Gaussian-mixture model can be a Deep Neural Network-Gaussian-Mixture Model (DNN-GMM) acoustic model.

Claims

1. A method for generating complementary acoustic models for performing automatic speech recognition system combination, the method comprising:

at a device with a processor and memory storing instructions for execution by the processor;

training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, wherein training the deep neural network comprises;

determining, using the set of training data, a set of optimal weighting values of the deep neural network; and

storing the set of optimal weighting values in the memory;

linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein training the Gaussian-mixture model comprises;

determining, using the set of training data, a set of optimal parameter values of the Gaussian-mixture model; and

storing the set of optimal parameter values in the memory.

2. The method of claim 1, wherein the hidden layer is a bottleneck layer, and wherein a number of units of the bottleneck layer is less than a number of units of the input layer.

3. The method of claim 2, wherein the bottleneck layer has 20 to 50 units.

4. The method of claim 2, wherein the bottleneck layer has 30 to 40 units.

5. The method of claim 1, wherein the first portion of the trained deep neural network is configured to perform a dimensionality reduction on a feature vector that is inputted at the input layer.

6. The method of claim 1, wherein linking the Gaussian-mixture model to the hidden layer is performed without severing a connection from the hidden layer to another layer of the trained deep neural network.

7. The method of claim 1, wherein any feature vector outputted from the hidden layer is received by a second portion of the trained deep neural network, the second portion of the trained deep neural network comprising the output layer.

8. The method of claim 7, wherein the second portion of the trained deep neural network further comprises a second hidden layer.

9. The method of claim 1, wherein in response to receiving at the input layer a feature vector representing one or more segments of a speech signal:

a first probability that the feature vector corresponds to a particular phoneme or sequence of phonemes is outputted from the output layer; and

a second probability that the feature vector corresponds to the particular phoneme or sequence of phonemes is outputted from the trained Gaussian-mixture model.

10. The method of claim 1, further comprising:

inputting at the input layer a feature vector representing one or more segments of a speech signal;

receiving a first probability that the feature vector corresponds to a particular phoneme or sequence of phonemes from the output layer; and

receiving a second probability that the feature vector corresponds to the particular phoneme or sequence of phonemes from the trained Gaussian-mixture model.

11. The method of claim 10, further comprising:

performing automatic speech recognition using the first probability to obtain a first transcription output;

performing automatic speech recognition using the second probability to obtain a second transcription output; and

performing automatic speech recognition system combination using the first transcription output and the second transcription output.

12. The method of claim 1, wherein the trained deep neural network is a deep neural network acoustic model, and wherein the first portion of the trained deep neural network and the trained Gaussian-mixture model form a Deep Neural Network-Gaussian-Mixture Model (DNN-GMM) acoustic model.

13. The method of claim 12, wherein a relative difference between an error rate of the deep neural network acoustic model and an error rate of the DNN-GMM acoustic model is less than 10 percent.

14. The method of claim 12, wherein an error rate of the DNN-GMM acoustic model with respect to the deep neural network acoustic model is greater than 1 percent.

15. The method of claim 12, wherein a number of hidden layers in the trained deep neural network and a number of parameters of the trained Gaussian-mixture model are such that a relative difference between an error rate of the deep neural network acoustic model and an error rate of the DNN-GMM acoustic model is less than 10 percent and an error rate of the DNN-GMM acoustic model with respect to the deep neural network acoustic model is greater than 1 percent.

16. The method of claim 1, wherein the set of training data includes a set of feature vectors, and wherein the set of feature vectors is labeled such that each feature vector of the set of feature vectors is associated with a target phoneme or sequence of phonemes.

17. The method of claim 16, wherein each feature vector of the set of feature vectors represents one or more segments of a speech signal.

18. A non-transitory computer-readable storage medium comprising instructions for:

training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, wherein training the deep neural network comprises;

determining, using the set of training data, a set of optimal weighting values of the deep neural network; and

storing the set of optimal weighting values in the memory;

linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein training the Gaussian-mixture model comprises;

determining, using the set of training data, a set of optimal parameter values of the Gaussian-mixture model; and

storing the set of optimal parameter values in the memory.

19. A computing device comprising:

one or more processors;

memory;

one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs including instructions for;

training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, wherein the means for training the deep neural network comprises;

determining, using the set of training data, a set of optimal weighting values of the deep neural network;

storing the set of optimal weighting values in the memory;

linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein the means for training the Gaussian-mixture model comprises;

determining, using the set of training data, a set of optimal parameter values of the Gaussian-mixture model; and

storing the set of optimal parameter values in the memory.

20. A method for generating complementary acoustic models for performing automatic speech recognition system combination, the method comprising:

at a device with a processor and memory storing instructions for execution by the processor;

training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, and wherein the trained deep neural network is a deep neural network acoustic model;

linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein the first portion of the trained deep neural network and the trained Gaussian-mixture model form a Deep Neural Network-Gaussian-Mixture Model (DNN-GMM) acoustic model.

21. The method of claim 20, wherein the hidden layer is a bottleneck layer, and wherein a number of units of the bottleneck layer is less than a number of units of the input layer.

22. The method of claim 20, wherein the first portion of the trained deep neural network is configured to perform a dimensionality reduction on a feature vector that is inputted at the input layer.

23. The method of claim 20, wherein linking the Gaussian-mixture model to the hidden layer is performed without severing a connection from the hidden layer to another layer of the trained deep neural network.

24. The method of claim 20, wherein any feature vector outputted from the hidden layer is received by a second portion of the trained deep neural network, the second portion of the trained deep neural network comprising the output layer.

25. A non-transitory computer-readable storage medium comprising computer-executable instructions for:

training a deep neural network using a set of training data, wherein the deep neural network comprises an input layer, an output layer, and a plurality of hidden layers disposed between the input layer and the output layer, and wherein the trained deep neural network is a deep neural network acoustic model;

linking a Gaussian-mixture model to a hidden layer of the trained deep neural network such that any feature vector outputted from the hidden layer is received by the Gaussian-mixture model; and

training the Gaussian-mixture model via a first portion of the trained deep neural network and using the set of training data, wherein the first portion of the trained deep neural network includes the input layer and the hidden layer, and wherein the first portion of the trained deep neural network and the trained Gaussian-mixture model form a Deep Neural Network-Gaussian-Mixture Model (DNN-GMM) acoustic model.