System and method for compressed domain estimation of the signal to noise ratio of a coded speech signal

1. A method comprising:

receiving, at a computing device, a speech signal having a bitstream and a signal-to-noise ratio (“SNR”) associated therewith; and

estimating the SNR directly from the bitstream or using a partial decoder that is configured to extract one or more parameters, the parameters including at least one of a fixed codebook gain, an adaptive codebook gain, a pitch lag, and a line spectral frequency (“LSF”) coefficient.

2. The method of claim 1 , further comprising:

determining if the SNR is above a pre-defined threshold.

3. The method of claim 1 , further comprising:

determining an amount of energy associated with each packet of the received speech signal using an energy predictor that includes a feature extractor and a regressor.

4. The method of claim 3 , wherein the feature extractor includes the one or more parameters, a difference of contiguous LSFs, and a logarithm of summed fixed codebook gains for all subframes.

5. The method of claim 3 , wherein the regressor includes a classification and regression tree (“CART”) or a deep belief network (“DBN”).

6. The method of claim 3 , further comprising:

training one or more energy regressor models with a labeled database.

7. The method of claim 3 , further comprising:

storing a sequence of energies at a buffering stage.

8. The method of claim 1 , further comprising:

applying a 2-component Gaussian mixture model (“GMM”) estimator including an expectation-maximization (“EM”) algorithm.

9. The method of claim 8 , wherein the EM algorithm is executed during a test phase and does not require pre-trained models.

10. The method of claim 8 , wherein a buffered sequence of energies in dB is an input to the Gaussian mixture model estimator is the buffered sequence of energies in dB.

11. The method of claim 8 , wherein a mean of each gaussian component is initialized with a minimum energy plus a random offset, and with a maximum energy minus a random offset.

12. The method of claim 8 , wherein a difference of means of the 2-component Gaussian mixture model (“GMM”) estimator is an estimate of the SNR of the speech signal.

13. The method of claim 1 , further comprising:

computing a confidence of an SNR estimation using a machine learning module associated with a confidence estimator.

14. The method of claim 13 , wherein the confidence estimator is configured to analyze a feature vector including a variance and a weight of each of the 2-component Gaussian mixture model, and the estimated SNR.

15. The method of claim 13 , wherein the confidence estimator includes a regressor, the regressor including at least one of a classification and regression tree (“CART”) or a deep belief network (“DBN”).

16. The method of claim 15 , wherein the regressor includes a training process.

17. A system comprising:

one or more computing devices configured to receive a speech signal having a bitstream and a signal-to-noise ratio (“SNR”) associated therewith, the one or more computing devices being further configured to estimate the SNR directly from the bitstream or using a partial decoder that is configured to extract one or more parameters, the parameters including at least one of a fixed codebook gain, an adaptive codebook gain, a pitch lag, and a line spectral frequency (“LSF”) coefficient.

18. The system of claim 17 , wherein the one or more processors are further configured to determine an amount of energy associated with each packet of the received speech signal using an energy predictor that includes a feature extractor and a regressor.

19. The system of claim 17 , wherein the one or more processors are further configured to apply a 2-component Gaussian mixture model (“GMM”) estimator including an expectation-maximization (“EM”) algorithm.

20. The system of claim 17 , wherein the one or more processors are further configured to compute a confidence of an SNR estimation using a machine learning module associated with a confidence estimator.