Multiple topic identification in human/human conversations
Authors: X. Bost, G. Senay, M. El-Bèze, R. De Mori
Date: 2018-12-18 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

The paper deals with the automatic analysis of real-life telephone
conversations between an agent and a customer of a customer care service (ccs).
The application domain is the public transportation system in Paris and the
purpose is to collect statistics about customer problems in order to monitor
the service and decide priorities on the intervention for improving user
satisfaction. Of primary importance for the analysis is the detection of themes
that are the object of customer problems. Themes are defined in the application
requirements and are part of the application ontology that is implicit in the
ccs documentation. Due to variety of customer population, the structure of
conversations with an agent is unpredictable. A conversation may be about one
or more themes. Theme mentions can be interleaved with mentions of facts that
are irrelevant for the application purpose. Furthermore, in certain
conversations theme mentions are localized in specific conversation segments
while in other conversations mentions cannot be localized. As a consequence,
approaches to feature extraction with and without mention localization are
considered. Application domain relevant themes identified by an automatic
procedure are expressed by specific sentences whose words are hypothesized by
an automatic speech recognition (asr) system. The asr system is error prone.
The word error rates can be very high for many reasons. Among them it is worth
mentioning unpredictable background noise, speaker accent, and various types of
speech disfluencies. As the application task requires the composition of
proportions of theme mentions, a sequential decision strategy is introduced in
this paper for performing a survey of the large amount of conversations made
available in a given time period. The strategy has to sample the conversations
to form a survey containing enough data analyzed with high accuracy so that
proportions can be estimated with sufficient accuracy. Due to the unpredictable
type of theme mentions, it is appropriate to consider methods for theme
hypothesization based on global as well as local feature extraction. Two
systems based on each type of feature extraction will be considered by the
strategy. One of the four methods is novel. It is based on a new definition of
density of theme mentions and on the localization of high density zones whose
boundaries do not need to be precisely detected. The sequential decision
strategy starts by grouping theme hypotheses into sets of different expected
accuracy and coverage levels. For those sets for which accuracy can be improved
with a consequent increase of coverage a new system with new features is
introduced. Its execution is triggered only when specific preconditions are met
on the hypotheses generated by the basic four systems. Experimental results are
provided on a corpus collected in the call center of the Paris transportation
system known as ratp. The results show that surveys with high accuracy and
coverage can be composed with the proposed strategy and systems. This makes it
possible to apply a previously published proportion estimation approach that
takes into account hypothesization errors .