Kopparapu S.K. Non-Linguistic Analysis of Call Center Conversations

Springer, 2015. — 87 p.Voice-based call centers or business process outsourcing units generate huge amounts of speech data everyday during their day-to-day operations. Large and diverse types of information are hidden in these natural language conversations, which is begging to be exploited. The whole area of voice analytics deals with the aspect of deriving usable information from the audio data.
Conventionally, speech data converted to text followed by natural language text processing has been used to derive analytics. However, this traditional process of analyzing audio conversations has several major limitations. On one hand, the fact remains that conversion of natural language spoken conversation into text is still maturing even for languages which are well researched and rich in language resources, like English, which hampers the process of analyzing call center audio conversations. On the other hand, understanding aspects of audio conversations by text analysis is not comprehensive. How does one distinguish a /thank you/ spoken in jest and sarcasm versus /thank you/ spoken with gratitude by analyzing just the text Thank You? Further from a call center perspective, many a times the audio conversation needs to be analyzed with the simple requirement to spot an abnormal call from a normal call in which case the process of speech to text conversion would be an overkill.
In this short monograph, we will dwell on how non-linguistics features associated with spoken conversation can be used to infer information embedded in the call conversation. While the use of non-linguistic analysis can give insight into important aspects of how the conversation happened without worrying about what is the actual linguistic content of the conversation. Additionally, non-linguistic analysis eliminates the need to adopt a not-so-reliable speech to text conversion process, which gives us the flexibility of being able to analyze conversation with little dependency on the content and language of conversation.Overview
Voice Analytics Process
Call Center Linguistic Analytics
Non-linguistic Speech Processing
Case Study
Conclusions
A: Informal Definitions
B: Computing Speaking Rate
C: Estimating P_**
D: Best Sample Size for Computing Real-Time Speaking Rate
E: Resource Deficient Language ASR
F: WER Conversational Speech