Speech Intelligibility

Patent metadata

Field	Value
Publication number	US-2016372133-A1
Application number	US-201615180202-A
Country	US
Kind code	A1
Filing date	Jun 13, 2016
Priority date	Jun 17, 2015
Publication date	Dec 22, 2016
Grant date	—

Abstract

A device including a processor and a memory is disclosed. 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 the input speech, a formant signal to noise ratio (SNR) estimator to calculate SNR estimates using the noise spectral estimates and speech spectral estimates within each formant detected in a speech spectrum. The memory also includes a formant boost estimator to calculate and apply a set of gain factors to each frequency component of the input speech such that the resulting SNR within each formant reaches a pre-selected target value.

First claim

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;
a formant signal to noise ratio (SNR) estimator to calculate SNR estimates using the noise spectral estimates and speech spectral estimates within each formant detected in the input speech; and
a formant boost estimator to calculate and apply a set of gain factors to each frequency component of the input speech such that the resulting SNR within each formant reaches a pre-selected target 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 Levinson-Durbin algorithm.

5 . The device of claim 1 , wherein the formant SNR estimator is configured to calculate the formant SNR estimates using a ratio of speech and noise sums of squared spectral magnitudes estimates over a critical band centered on a formant center frequency, wherein the critical band is a frequency bandwidth of an auditory filter.

6 . The device of claim 1 , wherein the set of gain factors is calculated by multiplying each formant segment in the input speech by a pre-selected factor.

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 and an output of the filter combined with the input speech is passed through the output limiting mixer.

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

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

10 . 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; calculating speech spectral estimates from the input speech; calculating formant signal to noise ratio (SNR) in the calculated noise spectral estimates and the speech spectral estimates; segmenting formants in the speech spectral estimates; and calculating formant boost factor for each of the formants based on the calculated formant boost estimates.

11 . The method of claim 10 , 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.

12 . The method of claim 10 , wherein the calculating the noise spectral estimates includes calculating the speech spectral estimates using a low order linear prediction filter.

13 . The method of claim 12 , wherein the low order linear prediction filter uses Levinson-Durbin algorithm.

14 . The method of claim 10 , wherein the calculating the formant SNR estimates includes using a ratio of speech and noise sums of squared spectral magnitudes estimates over a critical band centered on a formant center frequency, wherein the critical band is a frequency bandwidth of an auditory filter.

15 . The method of claim 10 , wherein the set of gain factors is calculated by multiplying each formant segment in the input speech by a pre-selected factor.

16 . A computer program product comprising instructions which, when being executed by a processor, cause said processor to carry out or control the method of claim 10 .

Assignees

• Nxp Bv

Inventors

• Daniel Adrien

Classifications

• G10L21/0364 Primary

  for improving intelligibility · CPC title

• G10L25/15

  the extracted parameters being formant information · CPC title

• G10L2019/0016

  Codebook for LPC parameters · CPC title

• G10L19/06

  Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients · CPC title

• G10L21/0264 Primary

  characterised by the type of parameter measurement, e.g. correlation techniques, zero crossing techniques or predictive techniques · CPC title