QUALITY-OF-EXPERIENCE MEASUREMENT FOR VOICE SERVICES

• US 20130051271A1
• Filed: 02/10/2011
• Published: 02/28/2013
• Est. Priority Date: 02/10/2011
• Status: Active Grant

• Current Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Cao, Junwei, Qian, Han, Li, Junwei
• Granted Patent: US 9,185,005 B2
• Time in Patent Office: Days
• US Class Current: 370/252
• CPC Class Codes: G10L 2025/906 Pitch tracking
• CPC Class Codes: G10L 25/09 the extracted parameters being zero crossing rates
• CPC Class Codes: G10L 25/60 for measuring the quality of voice signals
• CPC Class Codes: G10L 25/63 for estimating an emotional state
• CPC Class Codes: G10L 25/90 Pitch determination of speech signals
• CPC Class Codes: H04L 41/5067 Customer-centric QoS measurements
• CPC Class Codes: H04L 43/0852 Delays
• CPC Class Codes: H04L 65/1083 In-session procedures
• CPC Class Codes: H04L 65/80 Responding to QoS

Abstract

Technologies are generally described for determining a quality-of-experience (QoE) of a network communication. In some examples, a process for determining a QoE metric for a network communication includes receiving a media signal from the network communication. The process may also include extracting an experience indicator from the media signal, wherein the experience indicator corresponds to a participant'"'"'s subjective usage experience associated with a service quality of the network communication, and determining the QoE metric for the network communication based on the experience indicator and a quality-of-service (QoS) metric for the network communication.

Claims

1. A method for determining a quality-of-experience (QoE) metric for a network communication, comprising:

receiving a media signal from the network communication;

extracting an experience indicator from the media signal, wherein the experience indicator corresponds to a participant'"'"'s subjective usage experience associated with a service quality of the network communication; and

determining the QoE metric for the network communication based on the experience indicator and a quality-of-service (QoS) metric for the network communication.

2. The method as recited in claim 1, wherein the determining of the QoE metric further comprising

upon a determination that the experience indicator illustrating the service quality of the network communication is subpar, retrieving the QoS metric for the network communication;

and

upon a determination that the QoS metric illustrating the service quality of the network communication is subpar, calculating the QoE metric.

3. The method as recited in claim 1, wherein the experience indicator is a voice feature descriptive of the service quality of the network communication.

4. The method as recited in claim 1, wherein the voice feature is an emotion-change amplitude measurement.

5. The method as recited in claim 1, wherein the voice feature is a voice-pacing time measurement.

6. The method as recited in claim 1, wherein the voice feature is a sound-volume pitch measurement.

7. The method as recited in claim 1, wherein the experience indicator is a corpus descriptive of the service quality of the network communication.

8. The method as recited in claim 1, wherein the experience indicator is a facial expression descriptive of the service quality of the network communication.

9. The method as recited in claim 1, wherein the experience indicator is a body gesture descriptive of the service quality of the network communication.

10. A method for determining quality of a network communication, comprising:

receiving a media signal from the network communication;

extracting a first corpus from the media signal, wherein the first corpus corresponds to a participant'"'"'s subjective usage experience associated with a service quality of the network communication; and

determining a first QoE metric for the network communication based on the first corpus and a quality-of-service (QoS) metric for the network communication.

11. The method as recited in claim 10, wherein the extracting of the corpus from the media signal further comprising:

reducing noise from the received media signal;

extracting a plurality of voice segments from the media signal;

recognizing a plurality of words from the plurality of voice segments; and

identifying the first corpus based on the plurality of words.

12. The method as recited in claim 10, wherein the determining of the first QoE metric further comprising:

retrieving a second QoE metric for a second corpus, wherein the first corpus is subsequent to the second corpus in the media signal; and

initiate the first QoE metric based on the second QoE metric, the first corpus, and the QoS metric for the network communication.

13. The method as recited in claim 10, wherein the determining of the first QoE metric further comprising:

retrieving a second QoE metric for a second corpus, wherein the first corpus is subsequent to the second corpus in the media signal; and

exponentially increasing the first QoE metric based on the second QoE metric, the first corpus, and the QoS metric for the network communication.

14. The method as recited in claim 10, wherein the determining of the first QoE metric further comprising:

upon a determination that the corpus illustrating the service quality of the network communication is subpar, collecting the QoS metric of the network communication; and

upon a determination that the QoS metric illustrating the service quality of the network communication is subpar, calculating the QoE metric.

15. The method as recited in claim 10, wherein the service quality indicated by the first corpus has a delay condition, a decay condition, or a jitter condition.

16. A machine-readable medium having a set of instructions which, when executed by a processor, cause the processor to perform a method for determining a quality-of-experience (QoE) metric for a network communication, the method comprising:

receiving a media signal from the network communication;

extracting an experience indicator from the media signal, wherein the experience indicator corresponds to a participant'"'"'s subjective usage experience associated with a service quality of the network communication; and

determining the QoE metric for the network communication based on the experience indicator and a quality-of-service (QoS) metric for the network communication.

17. The machine-readable medium as recited in claim 16, wherein the determining of the QoE metric further comprising

upon a determination that the experience indicator illustrating the service quality of the network communication is subpar, retrieving the QoS metric for the network communication;

and

upon a determination that the QoS metric illustrating the service quality of the network communication is subpar, calculating the QoE metric.

18. The machine-readable medium as recited in claim 16, wherein the experience indicator is a voice feature descriptive of the service quality of the network communication.

19. The machine-readable medium as recited in claim 16, wherein the experience indicator is a facial expression descriptive of the service quality of the network communication.

20. The machine-readable medium as recited in claim 16, wherein the experience indicator is a corpus descriptive of the service quality of the network communication.