Loss tolerant speech decoder for telecommunications

• US 5,907,822 A
• Filed: 04/04/1997
• Issued: 05/25/1999
• Est. Priority Date: 04/04/1997
• Status: Expired due to Fees

• Current Assignee: Titan (L3Harris Technologies, Inc.)
• Original Assignee: LinCom Corp (LinQuest Corporation)
• Inventors: Prieto, Jaime L. Jr.
• Primary Examiner(s): Hudspeth, David R.
• Assistant Examiner(s): MCFADDEN, SUSAN IRIS
• Application Number:
  US08/833,287
• Time in Patent Office: 781 Days
• Field of Search: 704/202, 704/219, 704/221, 704/223, 704/226, 704/206, 704/259
• US Class Current: 704/202
• CPC Class Codes: G10L 19/005 Correction of errors induced by the transmission channel, if related to the coding algorithm

Abstract

A method and device for extrapolating past signal-history data for insertion into missing data segments in order to conceal digital speech frame errors. The extrapolation method uses past-signal history that is stored in a buffer. The method is implemented with a device that utilizes a finite-impulse response (FIR) multi-layer feed-forward artificial neural network that is trained by back-propagation for one-step extrapolation of speech compression algorithm (SCA) parameters. Once a speech connection has been established, the speech compression algorithm device begins sending encoded speech frames. As the speech frames are received, they are decoded and converted back into speech signal voltages. During the normal decoding process, pre-processing of the required SCA parameters will occur and the results stored in the past-history buffer. If a speech frame is detected to be lost or in error, then extrapolation modules are executed and replacement SCA parameters are generated and sent as the parameters required by the SCA. In this way, the information transfer to the SCA is transparent, and the SCA processing continues as usual. The listener will not normally notice that a speech frame has been lost because of the smooth transition between the last-received, lost, and next-received speech frames.

Claims

1. A loss-tolerant speech decoder that receives speech frame parameters according to a speech compression algorithm, said decoder comprising:

a frame error detector, said frame error detector capable of discriminating between properly received speech frame parameters and parameters that are lost or corrupted, said frame error detector further capable of issuing a signal upon receipt of lost or corrupted speech frame parameters,

a parameter decoder, said parameter decoder capable of decoding said received speech frame parameters to make decoded speech frames,

a buffer, said buffer used to store a history of said decoded speech frames received by said buffer from said parameter decoder,

a speech filter, said speech filter capable of generating replacement speech frame parameters that are written to said parameter decoder upon issuance of said signal from said frame error code detector upon receipt of a lost or corrupted speech frame,

wherein said replacement speech frame parameters take the place of lost or corrupted speech frame parameters received by said decoder in order to conceal said lost or corrupted speech frame parameters.

2. A speech decoder as in claim 1 wherein said speech filter has a plurality of neural networks.

3. A speech decoder as in claim 2 wherein said neural networks are multi-layer feed-forward neural networks.

4. A speech decoder as in claim 3 wherein said neural networks are finite-impulse response multi-layer feed-forward neural networks.

5. A speech decoder as in claim 2 wherein said neural networks are trained by the back-propagation method.

6. A speech decoder as in claim 5 wherein said back-propagation training includes the addition of input nodes.

7. A speech decoder as in claim 2 wherein at least one neural network is designated for the energy characteristics of said speech frame parameters.

8. A speech decoder as in claim 2 wherein at least one neural network is designated for the voicing characteristics of said speech frame parameters.

9. A speech decoder as in claim 2 wherein at least one neural network is designated for the pitch characteristics of said speech frame parameters.

10. A speech decoder as in claim 2 wherein at least one neural network is designated for the low frequency envelope characteristics of said speech frame parameters.

11. A speech decoder as in claim 2 wherein at least one neural network is designated for the medium frequency envelope characteristics of said speech frame parameters.

12. A speech decoder as in claim 2 wherein at least one neural network is designated for the high frequency envelope characteristics of said speech frame parameters.

13. A speech decoder as in claim 2 wherein said speech filter generates replacement speech frame parameters based upon said history of said decoded speech frames stored in said buffer.

14. A speech decoder as in claim 1 wherein said buffer receives decoded speech frame information from said speech filter.

15. A speech decoder as in claim 1 wherein a speech compression algorithm synthesizer receives decoded parameters from said parameter decoder and transforms said decoded parameters into speech signal voltages that are then output to a listener.

16. A speech decoder as in claim 1 wherein said replacement speech frame parameters from said speech filter are reformatted in a back-calculation device to conform to an input format of said parameter decoder before said replacement speech frame parameters are written to said parameter decoder.

17. A speech decoder as in claim 1 wherein said decoded parameters received by said parameter decoder are first reformatted in a calculation device to conform to a format acceptable to said buffer before being stored in said buffer.