Generating acoustic models

1. A system comprising:

one or more computer processors; and

one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, cause the one or more processors to perform operations, the operations comprising:

receiving, at a computer system, a request to generate or modify a target acoustic model for a target language;

accessing, by the computer system, a source acoustic model for a source language, wherein the source acoustic model includes information that maps acoustic features of the source language to phonemes in a transformed feature space;

aligning, using the source acoustic model in the transformed feature space, untransformed voice data in the target language with phonemes in a corresponding textual transcript to obtain aligned voice data, wherein the untransformed voice data is in an untransformed feature space;

transforming the aligned voice data according to a particular transform operation using the source acoustic model to obtain transformed voice data;

adapting the source acoustic model to the target language, using the untransformed voice data in the target language, to obtain an adapted acoustic model; and

training, by the computer system, a target acoustic model for the target language using the transformed voice data and the adapted acoustic model; and

providing the target acoustic model in association with the target language.

2. The system of claim 1 , wherein the transformed feature space of the source acoustic model comprises a Constrained Maximum Likelihood Linear Regression (CMLLR) feature space that is generated from a CMLLR transform operation.

3. The system of claim 1 , wherein the source acoustic model is generated from performance of a Linear Discriminant Analysis (LDA) transform operation, Vocal Tract Length Normalization (VTLN) transform operation, and a CMLLR transform operation on training data in the source language, wherein the training data comprises voice data in the source language and corresponding textual transcripts.

4. The system of claim 1 , wherein the particular transform operation comprises a VTLN transform operation that is performed on the aligned voice data using the source acoustic model.

5. The system of claim 1 , wherein the source acoustic model is adapted to the target language by performing a maximum a posteriori (MAP) adaptation operation on the source acoustic model using the untransformed voice data in the target language.

6. The system of claim 1 , wherein training the target acoustic model comprises performing a CMLLR transform operation and a maximum mutual information (MMI) transform operation using the transformed voice data and the adapted acoustic model.

7. The system of claim 1 , wherein the target language and the source language comprise different dialects of a common language.

8. The system of claim 1 , wherein the target language and the source language comprise languages than are different from each other.

9. A system comprising:

one or more computer processors; and

one or more non-transitory computer readable devices that include instructions that, when executed by the one or more computer processors, cause the one or more processors to perform operations, the operations comprising:

obtaining aligned voice data that comprises untransformed voice data in a target language that has been aligned with phonemes in a corresponding textual transcript using a source acoustic model for a source language, wherein the source acoustic model includes information that maps acoustic features of the source language to phonemes in a transformed feature space, and the untransformed voice data is in an untransformed feature space;

transforming the aligned voice data according to a particular transform operation, using the source acoustic model, to obtain transformed voice data;

adapting the source acoustic model to the target language using the untransformed voice data in the target language to obtain an adapted acoustic model; and

training a target acoustic model for the target language using the transformed voice data and the adapted acoustic model; and

providing the target acoustic model in association with the target language.

10. The system of claim 9 , wherein the transformed feature space of the source acoustic model comprises a Constrained Maximum Likelihood Linear Regression (CMLLR) feature space that is generated from a CMLLR transform operation.

11. The system of claim 9 , wherein the source acoustic model is generated from performance of a Linear Discriminant Analysis (LDA) transform operation, Vocal Tract Length Normalization (VTLN) transform operation, and a CMLLR transform operation on training data in the source language, wherein the training data comprises voice data in the source language and corresponding textual transcripts.

12. The system of claim 9 , wherein the particular transform operation comprises a VTLN transform operation that is performed on the aligned voice data using the source acoustic model.

13. The system of claim 9 , wherein the source acoustic model is adapted to the target language by performing a maximum a posteriori (MAP) adaptation operation on the source acoustic model using the untransformed voice data in the target language.

14. The system of claim 9 , wherein training the target acoustic model comprises performing a CMLLR transform operation and a maximum mutual information (MMI) transform operation using the transformed voice data and the adapted acoustic model.

15. The system of claim 9 , wherein the target language and the source language comprise different dialects of a common language.

16. The system of claim 9 , wherein the target language and the source language comprise languages that are different from each other.