1. On the Integration of Speech Recognition into Personal Networks
- ICSLP2004, Korea
Zheng-Hua Tan, Paul Dalsgaard and Børge Lindberg
{zt, pd,
Aalborg University, Denmark

2. The concept of personal networks (PN)
Outline
Motivation
The concept of personal networks (PN)
Speech recognition in personal networks
Robust against network degradations
Towards QoS driven spoken language systems – adaptation to networks
Frame-error-rate based OOV detection
Conclusions

3. What is the future network environment?
Motivation
Wireless communication presents a number of challenges to speech technology
limited resources available in the terminals
bandwidth constraints
transmission errors and packet losses
What is the future network environment?

4. The concept of personal networks (PN)
Home network
Internet, UMTS, WLAN, etc.
Smart building
Corporate
network
Vehicular area network
PAN
Core PAN

5. Speech recognition in personal networks
Terminal based vs. network based
Network based ASR
architecture
front-end
source coding
channel coding
Distributed speech recognition (DSR)

6. Feature-pair and SVQ: The n’th vector is
The ETSI-DSR standard
Feature-pair and SVQ: The n’th vector is
Frame-pair:
Feature-pair
Subvector2
8 bits
Subvector1
6 bits

7. Robust against network degradation
Analysis of error characteristics
Burst-like vs. random
Distribution functions of erroneous frames by length. Length ‘O’ covers lengths larger than 8.
100%
96%
56%

8. Robust against network degradation – cont.
Frame-pair vs. one-frame
Vector based vs. subvector based
One-frame based CRC
Subvector based EC

9. Robust against network degradation – cont.
Consistency matrix and subvector concealment
0 for inconsistent
1 for consistent
C =

10. Robust against network degradation – cont.
Experimental results – Aurora 2
Training: Clean speech training
Test: clean data from Test set A

11. FER-based out-of-vocabulary detection
OOV Detection
Reject the H0 hypothesis if
H0: one of the IV words
H1: OOV words modelled by one filler model
O: speech signal observation
T: threshold
IV words
OOV words
Two kinds of errors
False rejection (FR)
False acceptance (FA)

12. FER-based out-of-vocabulary detection – cont.
Transmission errors change the probability density of the log-likelihood ratio in two ways:
increasing the standard deviation
shifting the mean

13. FER-based out-of-vocabulary detection – cont.
A FER-dependent threshold for OOV detection
the threshold is modelled as a function of the frame-error-rate (FER)
Experiments
IV: Danish digits
OOV: city names
random bit errors
aimed at a constant FR
False rejection rate vs. AWGN channel BER values

14. Towards QoS driven spoken language systems - adaptation to networks
From front-end concealment to back-end adaptation to network degradations
further to QoS dependant adaptation of spoken language processing and dialogue management modules.
For example, the user can be requested to use a more restricted vocabulary and grammar or to switch to other modalities.

15. Shows the importance of applying a robust error concealment scheme
Conclusions
Reviews the developments of incorporating speech technology into a network architecture
distributed architecture of ASR
in a wide range of devices.
Shows the importance of applying a robust error concealment scheme
Presents a FER based OOV detection method
Points out that new research has to be initiated with the aim of introducing QoS-dependent modifications to existing ASR modules