Title page for ETD etd-06042010-124840
Stoop, Werner Email
An investigation into the feasibility of monitoring a call centre using an emotion recognition system
oproep sentrum verbetering
spraak kenmerk ontrekking
spraak segment isolasie
speech segment isolation
In this dissertation a method for the classification of emotion in speech recordings made in a customer service call centre of a large business is presented.
The problem addressed here is that customer service analysts at large businesses have to
listen to large numbers of call centre recordings in order to discover customer service-related
issues. Since recordings where the customer exhibits emotion are more likely to contain useful
information for service improvement than �neutral� ones, being able to identify those recordings should save a lot of time for the customer service analyst.
MTN South Africa agreed to provide assistance for this project. The system that has been
developed for this project can interface with MTN�s call centre database, download recordings,
classify them according to their emotional content, and provide feedback to the user.
The system faces the additional challenge that it is required to classify emotion notwith-
standing the fact that the caller may have one of several South African accents. It should also
be able to function with recordings made at telephone quality sample rates.
The project identifies several speech features that can be used to classify a speech recording
according to its emotional content. The project uses these features to research the general
methods by which the problem of emotion classification in speech can be approached. The
project examines both a K-Nearest Neighbours Approach and an Artificial Neural Network-
Based Approach to classify the emotion of the speaker.
Research is also done with regard to classifying a recording according to the gender of the speaker using a neural network approach. The reason for this classification is that the gender of a speaker may be useful input into an emotional classifier.
The project furthermore examines the problem of identifying smaller segments of speech
in a recording. In the typical call centre conversation, a recording may start with the agent
greeting the customer, the customer stating his or her problem, the agent performing an action,
during which time no speech occurs, the agent reporting back to the user and the call being
terminated. The approach taken by this project allows the program to isolate these different
segments of speech in a recording and discard segments of the recording where no speech occurs.
This project suggests and implements a practical approach to the creation of a classifier in a commercial environment through its use of a scripting language interpreter that can train a classifier in one script and use the trained classifier in another script to classify unknown recordings.
The project also examines the practical issues involved in implementing an emotional clas-
sifier. It addresses the downloading of recordings from the call centre, classifying the recording and presenting the results to the customer service analyst.
AFRIKAANS : n Metode vir die klassifisering van emosie in spraakopnames in die oproepsentrum van �n
groot sake-onderneming word in hierdie verhandeling aangebied.
Die probleem wat hierdeur aangespreek word, is dat kli�entediens ontleders in ondernemings
na groot hoeveelhede oproepsentrum opnames moet luister ten einde kli�entediens aangeleenthede
te identifiseer. Aangesien opnames waarin die kli�ent emosie toon, heel waarskynlik nuttige
inligting bevat oor diensverbetering, behoort die vermo�e om daardie opnames te identifiseer vir
die analis baie tyd te spaar.
MTN Suid-Afrika het ingestem om bystand vir die projek te verleen. Die stelsel wat ontwikkel
is kan opnames vanuit MTN se oproepsentrum databasis verkry, klassifiseer volgens
emosionele inhoud en terugvoering aan die gebruiker verskaf.
Die stelsel moet die verdere uitdaging kan oorkom om emosie te kan klassifiseer nieteenstaande
die feit dat die spreker een van verskeie Suid-Afrikaanse aksente het. Dit moet ook in
staat wees om opnames wat gemaak is teen telefoon gehalte tempos te analiseer.
Die projek identifiseer verskeie spraak eienskappe wat gebruik kan word om �n opname
volgens emosionele inhoud te klassifiseer.
Die projek gebruik hierdie eienskappe om die algemene metodes waarmee die probleem van
emosie klassifisering in spraak benader kan word, na te vors. Die projek gebruik �n K-Naaste
Bure en �n Neurale Netwerk benadering om die emosie van die spreker te klassifiseer.
Navorsing is voorts gedoen met betrekking tot die klassifisering van die geslag van die spreker
deur �n neurale netwerk. Die rede vir hierdie klassifisering is dat die geslag van die spreker �n
nuttige inset vir �n emosie klassifiseerder mag wees.
Die projek ondersoek ook die probleem van identifisering van spraakgedeeltes in �n opname.
In �n tipiese oproepsentrum gesprek mag die opname begin met die agent wat die kli�ent groet,
die kli�ent wat sy of haar probleem stel, die agent wat �n aksie uitvoer sonder spraak, die agent
wat terugrapporteer aan die gebruiker en die oproep wat be�eindig word. Die benadering van
hierdie projek laat die program toe om hierdie verskillende gedeeltes te isoleer uit die opname
en om gedeeltes waar daar geen spraak plaasvind nie, uit te sny.
Die projek stel �n praktiese benadering vir die ontwikkeling van �n klassifiseerder in �n
kommersi�ele omgewing voor en implementeer dit deur gebruik te maak van �n programeer taal
interpreteerder wat �n klassifiseerder kan oplei in een program en die opgeleide klassifiseerder
gebruik om �n onbekende opname te klassifiseer met behulp van �n ander program.
Die projek ondersoek ook die praktiese aspekte van die implementering van �n emosionele
klassifiseerder. Dit spreek die aflaai van opnames uit die oproep sentrum, die klassifisering
daarvan, en die aanbieding van die resultate aan die kli�entediens analis, aan.
Stoop, W 2010, An investigation into the feasibility of monitoring a call centre using an emotion recognition system, MEng dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://upetd.up.ac.za/thesis/available/etd-06042010-124840/ >