Title: Voice controlled electronic health record in dentistry Author: MUDr. Radek Hippmann Department: Department of paediatric stomatology, Faculty hospital Motol Supervisor: Prof. MUDr. Taťjana Dostalová, DrSc., MBA Supervisor's e-mail: Tatjana.Dostalova@fnmotol.cz This PhD thesis is concerning with development of the complex electronic health record (EHR) for the field of dentistry. This system is also enhanced with voice control based on the Automatic speech recognition (ASR) system and module for speech synthesis Text-to- speech (TTS). In the first part of the thesis is described the whole issue and are defined particular areas, whose combination is essential for EHR system creation in this field. It is mainly basic delimiting of terms and areas in the dentistry. In the next step we are engaged in temporomandibular joint (TMJ) problematic, which is often ignored and trends in EHR and voice technologies are also described. In the methodological part are described delineated technologies used during the EHR system creation, voice recognition and TMJ disease classification. Following part incorporates results description, which are corresponding with the knowledge base in dentistry and TMJ. From this knowledge base originates the graphic user interface DentCross, which is serving for dental data...

Denna studie undersöker anställdas inställning till BA som MFA-metod på svenska SMF, även vilken BA-metod som anses lämpligast för användning. Tidigare studier har främst fokuserat på större organisationer och företag med kvantitativa forskningsmetoder. Därav har denna studie valt att undersöka SMF med en kvalitativ inriktning i formen av en semistrukturerad intervjustudie. Studien genomförde totalt nio intervjuer från varierande företag med respondenter aven mängd olika arbetsroller. Utifrån dessa intervjuer kunde flera teman identifieras, dessa användes för att dra slutsatser kring användarens inställning och vilken BA-metod som föredras i företagskontext. Teman som identifierades utgjorde grunden förde anställdas inställning, exempelvis vilka positiva och negativa aspekter de anställda kunde se med BA som MFA. Resultaten visade att de anställda generellt sett var positivt inställda till BA som MFA-metod och att de flesta kunde tänka sig implementera detta på sin arbetsplats. Av fingeravtryck, röstigenkänning,ansiktsigenkänning och irisskanning föredrog respondenterna fingeravtryck som BA-metod i företagskontext. Studien valde att inte fokusera på företag som redan implementerat BA som MFA. Applikationer och program som anställda använder där BA erbjuds anses inte som att företaget implementerat BA som MFA. / This study examines employees' attitudes towards BA (Biometric Authentication) as an MFA (Multi-Factor Authentication) method at Swedish SMEs, as well as which BA method is considered most suitable for use. Previous studies have focused on larger organizations and companies with quantitative research methods. Therefore, this study has chosen to examine SMEs with a qualitative focus by conducting semistructured interviews. The study conducted a total of nine interviews from various companies with respondents from a variety of work roles. Based on these interviews, several themes could be identified, these were used to draw conclusions about the employees’ attitude and which BA method is preferred in a business context. Themes that were identified formed the basis for the employees' attitude, for example what positive and negative aspects the employees could see with BA as MFA. The results showed that the employees were generally positive about BA as an MFA method and that most could imagine implementing this in their workplace. Focusing on fingerprints, voice recognition, face recognition and iris scanning, the respondents preferred fingerprints as a BA method. The study chose not to focus on companies that have already implemented BA as MFA. Applications and programs that employees use where BA is offered are not considered that the company has implemented BA as MFA.

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Approved for public release, distribution is unlimited / This study identifies factors affecting the performance of commercial-off-the-shelf speech recognition software (SRS) when used for ship control purposes. After a review of research in the feasibility and acceptability of SRS-based ship control, the paper examines the effects of: "A restricted vocabulary versus a large vocabulary," Low experience level conning officers versus high experience level conning officers, "Male versus female voices," Pre-test training on specific words versus no pre-test training. Controlled experimentation finds that: "The experience level of a conning officer has no significant impact on SRS performance." Female participants experienced more SRS errors than did their male counterparts. However, in this experiment, only a limited number of trials were available to assess a difference. "SRS with restricted vocabulary performs no better than SRS with large vocabularies." Using the software "correct as you go" feature may impact software performance. Following the user profile establishment, individual user training on two specific words reduces error rates significantly. This study concludes that SRS is a viable technology for ship control and merits further testing and evaluation. / Lieutenant, United States Navy

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

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Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal

Submitted by Johnny Rodrigues (johnnyrodrigues@ufcg.edu.br) on 2018-08-20T20:10:09Z No. of bitstreams: 1 GUTEMBERG GONÇALVES DOS SANTOS JÚNIOR - DISSERTAÇÃO PPGEE 2009..pdf: 2756190 bytes, checksum: 5812d37f7ad4c18eb26e9672d4890812 (MD5) / Made available in DSpace on 2018-08-20T20:10:09Z (GMT). No. of bitstreams: 1 GUTEMBERG GONÇALVES DOS SANTOS JÚNIOR - DISSERTAÇÃO PPGEE 2009..pdf: 2756190 bytes, checksum: 5812d37f7ad4c18eb26e9672d4890812 (MD5) Previous issue date: 2009-05-08 / O estabelecimento de uma interface de comunicação através da voz entre seres humanos e computadores vem sendo perseguido desde o início da era da computação. Nesta direção, diversos avanços foram realizados nas últimas seis décadas, permitindo o uso comercial de aplicações com reconhecimento de voz nos dias atuais. Entretanto, fatores como ruídos, reverberações, distorções entre outros, comprometem o desempenho desses sistemas ao reduzir a taxa de acerto quando submetidos a ambientes adversos. Assim, o estudo de técnicas que diminuam os efeitos desses problemas é de grande valia e vem ganhando destaque nas últimas décadas. O trabalho apresentado nesta dissertação tem como objetivo a redução dos problemas referentes aos ruídos característicos de ambientes automotivos, tornando os sistemas de reconhecimento de voz utilizados nesses ambientes mais robustos. Dessa forma, o controle de funcionalidades não-críticas de um automóvel, ou seja, funcionalidades que não coloquem em risco a vida do usuário como tocadores de música e ar condicionado, pode ser realizado através de comandos de voz. O sistema proposto é baseado numa etapa de pré-processamento do sinal de voz através do método de subespaços vetoriais. O desempenho deste método está diretamente relacionado com as dimensões (linhas× colunas) das matrizes representativas do sinal de entrada. Levando isso em consideração, a decomposição ULLV, apesar de se tratar de uma aproximação do método de subespaços vetoriais, foi utilizada por oferecer uma menor complexidade computacional quando comparada a métodos tradicionais baseados na decomposição SVD. O sistema de reconhecimento de voz Julius foi o escolhido para o estudo de caso por se tratar de um sistema desenvolvido em código livre que oferece um alto desempenho. Um banco de dados de voz com 44800 amostras foi gerado com o modelo de um ambiente automotivo. Por fim, a robustez do sistema foi avaliada e comparada com um método tradicional de redução de ruído chamado subtração espectral. / The establishment of a speech-based communication interface between humans and computers has been pursued since the beginning of the computer era. Several studies have been made over the last six decades in order to accomplish this interface, making possible commercial use of speech recognition applications. However, factors such as noise, reverberation, distortion among others degrades the performance of these systems. Thus, reducing their success rate when operating in adverse environments. With this in mind, the study of techniques to reduce the impact of these problems is of a great value and has gained prominence in recent decades. The work presented in this dissertation aims to reduce problems related to noise encountered in an automotive environment, improving the speech recognition system robustness. Thus,controlofnon-critical features of a car, such as CD player and air conditioning, can be performed through voice commands. The proposed system is based on a speech signal preprocessing step using the signal subspace method. Its performance is related to the size (lines× columns) of the matrices that represents the input signal. Therefore, the ULLV decomposition was used because it offers a lower computational complexity compared to traditional methods based on SVD decomposition. The speech recognizer Julius is an open source software that offers high performance and was the chosen one for the case study. A noisy speech database with 44800 samples was generated to model the automotive environment. Finally, the robustness of the system was evaluated and compared with a traditional method of noise reduction called spectral subtraction.

The amount and variety of visual information presented on electronic displays is ever-increasing. Finding and acquiring relevant information in the most effective manner possible is of course desirable. While there are advantages to presenting a large number of information objects on a screen at the same time, it can also hinder fast detection of objects of interest. One way of addressing that problem is Voice Assisted Visual Search (VAVS). A user supported by VAVS calls out an object of interest and is immediately guided to the object by a highlighting cue. This thesis is an initial study of the VAVS user interface technique. The findings suggest that VAVS is a promising approach, supported by theory and practice. A working prototype shows that locating objects of interest can be sped up significantly, requiring only half the amount of time taken without the use of VAVS, on average. / Voice-Assisted Visual Search

Ljudigenkänning möjliggörs genom spektralanalys, som beräknas av den snabba fouriertransformen (FFT), och har under senare år nått stora genombrott i samband med ökningen av datorprestanda och artificiell intelligens. Tekniken är nu allmänt förekommande, i synnerhet inom bioakustik för identifiering av djurarter, en viktig del av miljöövervakning. Det är fortfarande ett växande vetenskapsområde och särskilt igenkänning av fågelsång som återstår som en svårlöst utmaning. Även de främsta algoritmer i området är långt ifrån felfria. I detta kandidatexamensarbete implementerades och utvärderades enkla algoritmer för att para ihop ljud med en ljuddatabas. En filtreringsmetod utvecklades för att urskilja de karaktäristiska frekvenserna vid fem tidsramar som utgjorde basen för jämförelsen och proceduren för ihopparning. Ljuden som användes var förinspelad fågelsång (koltrast, näktergal, kråka och fiskmås) så väl som egeninspelad mänsklig röst (4 unga svenska män). Våra resultat visar att framgångsgraden normalt är 50–70%, den lägsta var fiskmåsen med 30% för en liten databas och den högsta var koltrasten med 90% för en stor databas. Rösterna var svårare för algoritmen att särskilja, men de hade överlag framgångsgrader mellan 50% och 80%. Dock gav en ökning av databasstorleken generellt inte en ökning av framgångsgraden. Sammanfattningsvis visar detta kandidatexamensarbete konceptbeviset bakom fågelsångigenkänning och illustrerar såväl styrkorna som bristerna av dessa enkla algoritmer som har utvecklats. Algoritmerna gav högre framgångsgrad än slumpen (25%) men det finns ändå utrymme för förbättring eftersom algoritmen vilseleddes av ljud av samma frekvenser. Ytterligare studier behövs för att bedöma den utvecklade algoritmens förmåga att identifiera ännu fler fåglar och röster. / Sound recognition is made possible through spectral analysis, computed by the fast Fourier transform (FFT), and has in recent years made major breakthroughs along with the rise of computational power and artificial intelligence. The technology is now used ubiquitously and in particular in the field of bioacoustics for identification of animal species, an important task for wildlife monitoring. It is still a growing field of science and especially the recognition of bird song which remains a hard-solved challenge. Even state-of-the-art algorithms are far from error-free. In this thesis, simple algorithms to match sounds to a sound database were implemented and assessed. A filtering method was developed to pick out characteristic frequencies at five time frames which were the basis for comparison and the matching procedure. The sounds used were pre-recorded bird songs (blackbird, nightingale, crow and seagull) as well as human voices (4 young Swedish males) that we recorded. Our findings show success rates typically at 50–70%, the lowest being the seagull of 30% for a small database and the highest being the blackbird at 90% for a large database. The voices were more difficult for the algorithms to distinguish, but they still had an overall success rate between 50% and 80%. Furthermore, increasing the database size did not improve success rates in general. In conclusion, this thesis shows the proof of concept and illustrates both the strengths as well as short-comings of the simple algorithms developed. The algorithms gave better success rates than pure chance of 25% but there is room for improvement since the algorithms were easily misled by sounds of the same frequencies. Further research will be needed to assess the devised algorithms' ability to identify even more birds and voices.