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BISON Report Summary

Project ID: 645323
Funded under: H2020-EU.

Periodic Reporting for period 1 - BISON (BIg Speech data analytics for cONtact centres)

Reporting period: 2015-01-01 to 2015-12-31

Summary of the context and overall objectives of the project

According to European Contact Center Benchmark data, the European Contact Center (CC) industry involves more than 35,000 contact centers with 3.2 Million jobs, this represents 1% of Europe’s. The whole industry is extremely competitive, working with minimum business margins and stringent performance indicators. A usual CC operation generates a wealth of spoken data. A typical contact center with 1,000 agents, each doing 40 calls a day with an average call lasting for 3 minutes, generates 2,000 hours of audio every 24 hours. This data is the core of CC’s business; however, its current exploitation is rather limited.

The objective of BISON is to create a multi-lingual, modular and highly versatile software system for big speech data analytics in contact centers targeting:
* basic speech data mining technologies
* transforming the basic data into information valuable for business strategies
* real-deployment of the systems by real CCs

An important aspect of BISON is the relation between technology and law – privacy and legal aspects are considered as a solid grounding for what the technology should and should not be allowed to do. The technology is also considered as a valuable tool to check and enforce data privacy in a sensitive CC environment.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

In the first reporting period (2015), the progress in key axes of the project was the following:

On the level of basic speech data mining technologies, we have summarized the state of the art in laboratory and production techniques for speech data mining (including transcription, keyword spotting, speaker recognition and language identification) and prepared a clear scheme how to incorporate and motivate contact center users towards improvements of these technologies. Guidelines for collection of data needed for adaptation of speech mining to CC domain were created and this adaptation was started on the first set of languages. We have also consolidated or developed a set of production-grade speech mining technologies ready for integration.

For transforming the basic data into information valuable for business strategies, we have gathered user requirements, prioritized them and determined the level of technical and legal feasibility. Concurrently, we have carefully studied the legal and ethical issues concerning personal data processing, developing the legal framework required to proceed with the technical development in a fully law-abiding way. At the implementation level, we have determined sets of keywords pertaining to different scenarios, and for some, produced versions in multiple relevant languages, and developed the first version of tracking the CC call-flow and adherence to script, that are very important quality indicators.

Finally, on the level of real-deployment of the systems by real CCs, we have designed the architecture of a simple, but working CC call data mining solution, integrated with CC call handling infrastructure, including the definition of necessary APIs. We have worked on legal ways to transfer the data to, within and from the CC call data mining solution. Finally, the the solution was integrated into the first project demonstrator - smallBison. We have been continuously gathering feedback with current versions of CC speech data handling solutions (Phonexia’s SPAS and MyForce’s CTArchitect) in the consortium and outside, and got prepared to gather feedback based on smallBison.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

In 2015, the most important progress beyond state-of-the-art were
* starting Direct involvement of user in the functionality of recognition systems, by providing target annotated data and running selected adaptation steps.
* proposing solution for analysis of 100% of recorded calls, countering the current scheme of only partial assessment of CC by supervisors.
* starting assessing the call flow, directly related to agent’s quality and productivity.
* development of a demonstrator showcasing the integration of speech data mining with a CC infrastructure.
* laying solid legal and ethical grounds for data processing, data transfers and technology development within the CCs.

Related information

Record Number: 186626 / Last updated on: 2016-07-14
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