System and method for compressed domain language identification

Patent metadata

Field	Value
Publication number	US-9530400-B2
Application number	US-201414499867-A
Country	US
Kind code	B2
Filing date	Sep 29, 2014
Priority date	Sep 29, 2014
Publication date	Dec 27, 2016
Grant date	Dec 27, 2016

Abstract

Embodiments included herein are directed towards a system and method for compressed domain language identification. Embodiments may include receiving a bitstream of a sequence of packets at one or more computing devices and classifying each packet into speech or non-speech based upon, at least in part, compressed domain voice activity detection (VAD). Embodiments may further include extracting a pseudo-cepstral representation from the speech detected packets and partially decoding without extracting a PCM format and generating a sequence of multi-frames, based upon, at least in part, the pseudo-cepstral representation. Embodiments may also include providing in real time the sequence of multi-frames to a deep neural network (DNN), wherein the DNN has been trained off-line for one or more desired target languages.

First claim

What is claimed is:

1. A compressed domain language identification method comprising:
receiving a bitstream of a sequence of packets at one or more computing devices;
classifying each packet into speech or non-speech based upon, at least in part, compressed domain voice activity detection (VAD);
extracting a pseudo-cepstral representation from the speech detected packets and partially decoding without extracting a PCM format;
generating a sequence of multi-frames, based upon, at least in part, the pseudo-cepstral representation; and
providing in real time the sequence of multi-frames to a deep neural network (DNN), wherein the DNN has been trained off-line for one or more desired target languages.

2. The method of claim 1 , further comprising:
automatically detecting a language associated with the bitstream of the sequence of packets.

3. The method of claim 1 , wherein extracting includes extracting from a non-decoded bitstream of the sequence of packets.

4. The method of claim 1 , wherein extracting includes a delta computation and a delta-delta computation.

5. The method of claim 1 , further comprising:
discarding one or more non-speech frames using a compressed domain voice activity detector (VAD).

6. The method of claim 1 , wherein the DNN is configured to classify the bitstream as belonging to a particular language.

7. The method of claim 6 , further comprising:
averaging a logarithm of posterior possibilities of the DNN across one or more packets to detect the particular language.

8. The method of claim 1 , further comprising:
detecting a language associated with the bitstream of the sequence of packets without decoding into a pulse code modulated format.

9. The method of claim 1 , further comprising:
receiving an output score from the DNN; and
combining the output score with a pulse code modulated (PCM) language identification system score.

10. The method of claim 6 , further comprising:
performing an action based upon the classification, wherein the action includes at least one of, automatically routing a call in a call center, non-intrusively obtaining statistics regarding language usage in a network, and performing speaker identification.

11. A system for compressed domain language identification, the system including at least one processor configured to perform operations comprising:
receiving a bitstream of a sequence of packets at one or more computing devices;
classifying each packet into speech or non-speech based upon, at least in part, compressed domain voice activity detection (VAD);
extracting a pseudo-cepstral representation from the speech detected packets and partially decoding without extracting a PCM format;
generating a sequence of multi-frames, based upon, at least in part, the pseudo-cepstral representation; and
providing in real time the sequence of multi-frames to a deep neural network (DNN), wherein the DNN has been trained off-line for one or more desired target languages.

12. The system of claim 11 , further comprising:
automatically detecting a language associated with the bitstream of the sequence of packets.

13. The system of claim 11 , wherein extracting includes extracting from a non-decoded bitstream of the sequence of packets.

14. The system of claim 11 , wherein extracting includes a delta computation and a delta-delta computation.

15. The system of claim 11 , further comprising:
discarding one or more non-speech frames using a compressed domain voice activity detector (VAD).

16. The system of claim 11 , wherein the DNN is configured to classify the bitstream as belonging to a particular language.

17. The system of claim 16 , further comprising:
averaging a logarithm of posterior possibilities of the DNN across one or more packets to detect the particular language.

18. The system of claim 11 , further comprising:
detecting a language associated with the bitstream of the sequence of packets without decoding into a pulse code modulated format.

19. The system of claim 11 , further comprising:
receiving an output score from the DNN; and
combining the output score with a pulse code modulated (PCM) language identification system score.

20. The system of claim 16 , further comprising:
performing an action based upon the classification, wherein the action includes at least one of, automatically routing a call in a call center, non-intrusively obtaining statistics regarding language usage in a network, and performing speaker identification.

Assignees

• Nuance Communications Inc

Inventors

• Lainez Jose
• Barreda Daniel Almendro

Classifications

• H04M2203/2061

  Language aspects · CPC title

• H04M2242/12

  Language recognition, selection or translation arrangements · CPC title

• H04M3/51

  Centralised call answering arrangements requiring operator intervention {, e.g. call or contact centers for telemarketing} · CPC title

• G10L15/16

  using artificial neural networks · CPC title

• H04M3/42

  Systems providing special services or facilities to subscribers (specially adapted for wireless communication networks H04W4/00) · CPC title