Method and device for boosting formants from speech and noise spectral estimation

1. A device, comprising:

a processor;

a memory, wherein the memory includes:

a noise spectral estimator to calculate noise spectral estimates from a sampled environmental noise;

a speech spectral estimator to calculate speech spectral estimates from a input speech signal, wherein the sampled environmental noise is not noise present in the input speech signal;

a formant segmentation module configured to detect local minima in the speech spectral estimates and to define a formant as a spectral segment between two local minima, wherein the formant segmentation module is further configured to detect local minima in the speech spectral estimates by balancing the speech spectral estimates, differentiating the balanced speech spectral estimates, locating sign changes from negative to positive values in the values of the differentiated balanced speech spectral estimates, and marking the locations of the sign changes as local minima, wherein balancing the speech spectral estimates comprises computing a smoothed version of the speech spectral estimates and subtracting the smoothed version of the speech spectral estimates from the speech spectral estimates;

a formant signal to noise ratio (SNR) estimator to calculate a set of formant-specific SNR estimates using the noise spectral estimates and speech spectral estimates within each formant detected in the input speech signal, wherein the formant SNR estimator is configured to calculate each formant-specific SNR estimate in the set of formant-specific SNR estimates using a ratio of speech and noise sums of squared spectral magnitude estimates over a critical band centered on a formant center frequency, wherein the critical band is a frequency bandwidth of an auditory filter; and

a formant boost estimator to calculate a set of formant-specific gain factors from the set of formant-specific SNR estimates and to independently apply the set of formant-specific gain factors to each formant detected in the input speech signal such that the resulting SNR within each formant reaches a pre-selected formant-specific target SNR value.

2. The device of claim 1 , wherein the noise spectral estimator is configured to calculate noise spectral estimates through averaging, using a smoothing parameter and past spectral magnitude values obtained through a Discrete Fourier Transform of the sampled noise.

3. The device of claim 1 , wherein the speech spectral estimator is configured to calculate the speech spectral estimates using a low order linear prediction filter.

4. The device of claim 3 , wherein the low order linear prediction filter uses a Levinson-Durbin Algorithm.

5. The device of claim 1 , wherein the formant-specific gain factors are calculated by multiplying each formant in the input speech signal by a pre-selected factor.

6. The device of claim 5 , wherein the each formant in the speech input signal is detected by a formant segmentation module, wherein the formant segmentation module segments the speech spectral estimates into formants.

7. The device of claim 1 , further including an output limiting mixer, wherein the formant boost estimator produces a filter to filter the input speech signal and an output of the filter combined with the input speech signal is passed through the output limiting mixer.

8. The device of claim 7 , further including a formant unmasking filter to filter the input speech signal and to input an output of the formant unmasking filter to the output limiting mixer.

9. The device of claim 1 , wherein the formant segmentation module is further configured to create a piecewise linear signal from the marked locations and to subtract the piecewise linear signal from a corresponding balanced speech spectral envelope to obtain a normalized spectral envelope in which all local minima equal 0 dB.

10. The device of claim 1 , wherein the smoothed version of the speech spectral estimates is computed using cepstrum low-frequency filtering.

11. A method for performing an operation of improving speech intelligibility, comprising:

receiving an input speech signal;

calculating noise spectral estimates from a sampled environmental noise, wherein the sampled environmental noise is not noise present in the input speech signal;

calculating speech spectral estimates from the input speech signal;

segmenting formants in the speech spectral estimates by detecting local minima in the speech spectral estimates, wherein a formant is defined as a spectral segment between two local minima, wherein segmenting formants in the speech spectral estimates comprises detecting local minima in the speech spectral estimates by balancing the speech spectral estimates, differentiating the balanced speech spectral estimates, locating sign changes from negative to positive values in the values of the differentiated balanced speech spectral estimates, and marking the locations of the sign changes as local minima, wherein balancing the speech spectral estimates comprises computing a smoothed version of the speech spectral estimates and subtracting the smoothed version of the speech spectral estimates from the speech spectral estimates;

calculating a set of formant-specific signal to noise ratio (SNR) estimates using the calculated noise spectral estimates and the speech spectral estimates, wherein each formant-specific SNR estimate in the set of formant-specific SNR estimates is calculated using a ratio of speech and noise sums of squared spectral magnitude estimates over a critical band centered on a formant center frequency, wherein the critical band is a frequency bandwidth of an auditory filter;

calculating formant-specific gain factors for each of the formants based on the calculated set of formant-specific SNR estimates such that the resulting SNR within each formant reaches a pre-selected formant-specific target SNR value; and

applying the formant-specific gain factors individually to each formant.

12. The method of claim 11 , wherein the noise spectral estimates are calculated through a process of averaging, using a smoothing parameter and past spectral magnitude values obtained through a Discrete Fourier Transform of the sampled environmental noise.

13. The method of claim 11 , wherein the calculating the noise spectral estimates includes calculating the speech spectral estimates using a low order linear prediction filter.

14. The method of claim 13 , wherein the low order linear prediction filter uses a Levinson-Durbin Algorithm.

15. The method of claim 11 , wherein the formant-specific gain factors are calculated by multiplying each formant in the input speech signal by a pre-selected factor.

16. A non-transitory computer-readable medium that stores computer readable instructions which, when executed by a processor, cause said processor to carry out or control the method of claim 11 .

17. The method of claim 11 , wherein segmenting formants in the speech spectral estimates comprises creating a piecewise linear signal from the marked locations and subtracting the piecewise linear signal from a corresponding balanced speech spectral envelope to obtain a normalized spectral envelope in which all local minima equal 0 dB.

18. The method of claim 11 , wherein the smoothed version of the speech spectral estimates is computed using cepstrum low-frequency filtering.