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Article : Speech Analytics: The Technology Behind the Magic

'Vice President, Sales and Operations'
ASC
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General Overview

Competitive contact centers must research and implement solutions leveraging all fundamental speech recognition technologies, including phonetic indexing, large vocabulary continuous speech recognition (LVCSR), and keyword and phrase spotting.

Sophisticated contact centers are frequently combining these interrelated technologies to ensure flexibility, high levels of accuracy and unlimited scalability.

Contact centers exploring speech analytics technologies for the first time must understand the underlying technologies and how they will best support business requirements.

Phonetic Indexing

Phonetic indexing technologies generate a time-aligned phonetic index of analyzed conversation audio. This index is based on phonemes, the distinct components of every language. Because phonemes simply consist of uttered sounds, the indexing is unaffected by background noise, languages, dialects or speaking styles.

The phonetic index can be searched directly for contained words or phrases using special operators such as Boolean strings or time-based proximity to other content. A proprietary search engine identifies and matches the phonetic equivalent of the search string and returns relevancy-ranked results. When words or business logic need to be changed, the system can rapidly re-query the index without re-processing the audio.

Phonetic indexing creates the truest representation of spoken audio and enables the fastest, most accurate access to the information contained in audio files.

Transcription - LVCSR

LVCSR recognizes uttered sounds much like phonetic indexing, but subsequently, it matches combinations of phonemes against linguistic models containing a large human-language vocabulary to build a complete database and transcript of conversation audio.

All text produced by this process can easily be analyzed to facilitate the rapid discovery of interaction content, identification of new and emerging topics, quantification of related phrases, and automatic categorization of conversations by topic based on definable business logic.

LVCSR enables data mining and natural language processing for deep analysis and understanding of root causes of unknown issues. However, the special processing resources required to align spoken phonemes with matching words in a large dictionary is both time consuming and resource intensive.

Keyword and Phrase Spotting

Similar to LVCSR and phonetic indexing, keyword and phrase spotting recognizes uttered sounds, but subsequently, it matches combinations of phonemes against linguistic models containing a small vocabulary of critical keywords and phrases.

As a result, keyword and phrase spotting works much faster than LVCSR-based transcription and rapidly identifies conversations relevant for critical business insights. Keyword and phrase spotting closely monitors processes, enables compliance with government regulations, improves customer service, and provides transaction verification.

Best Practices Questions

Prior to selecting a speech analytics technology, you must identify fundamental business requirements driving investment in a speech analytics solution including:

What business outcomes do I want to achieve with a speech analytics solution?

  • Root Causes Analysis
  • Trend Analysis
  • Quality Assurance
  • Script Adherence
  • Compliance
  • Emotion Detection
  • Talk Analysis

How quickly should data be made available from analyzed conversations?

  • Real-time - I often need critical insights immediately in a rapidly changing environment.
  • Historical - I often need to deeply understand historical issues over time.

How frequently will requirements for your use of the speech analytics solution change?

  • Rarely - I am focused on compliance adherence with a use case that rarely changes.
  • Frequently - I am interested in new and emerging topics as they occur.

Are there manual, labor-intensive processes today that I would like to displace?

Conclusion

Clarity of objectives is critical from the outset of the decision making process to ensure alignment with the underlying technology best suited to support your business requirements, and to ensure buy-in and clear understanding of the return on investment throughout the implementation and optimization of the solution. Understanding the benefits and limitations of phonetic indexing, LVCSR and keyword and phrase spotting will go a long way in tailoring your speech analytics system for maximum advantage.


About ASC:
Company LogoWith the solutions from ASC, users can increase their service quality, protect their assets and react optimally in emergencies. Challenges of this kind are part of daily business for companies and public safety organizations. With ASC at their side, they benefit from the practice-oriented approach of ASC to not only capture communications streams, but also to structure this flood of data into valuable information which enhances productivity and decreases costs. ASC is a worldwide software provider of omni-channel recording, quality management and analytics. We address all enterprises with recording needs, especially contact centers, financial institutions and public safety organizations. We record, analyze and evaluate interactions across all media, including fixed line and mobile voice, chat, video, screen and SMS. The content of communication becomes accessible and critical information and trends are revealed, providing real-time business intelligence for immediate management action. ASC offers its entire portfolio either as on-premise or as a service provider with a cloud solution. We believe in cloud, and thus offer a truly multi-tenant, highly scalable family of products and services – giving our customers full flexibility, allowing them to invest as they grow, always leveraging our latest technology. With subsidiaries in the United Kingdom, France, Switzerland, Romania, Dubai, United States, Brazil, Mexico, H
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Today's Tip of the Day - Don’t Allow Scapegoats

Read today's tip or listen to it on podcast.

Published: Monday, September 21, 2015

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