A speech visualization system is proposed thatcould be used by a deaf person for understanding speech.Several novel techniques are proposed, including: (1) Minimizing spectral leakage in the Fourier transform by using avariable-length window. (2) Making use of the fact that there is no spectral leakage in order to calculate how much of the energy of the speech signal is due to its periodic component vs. its nonperiodic component. (3) Modelling the mouth and lips as a band-pass filter and estimating the central frequency and bandwidth of this filter in order to assign colours tounvoiced speech sounds.

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. Copyright / Dissertation (MEng)--University of Pretoria, 2010. / Electrical, Electronic and Computer Engineering / unrestricted