Configuration and Assessment of a Senior Level Course in Biometric Systems

It is very important that modern topics be covered at the senior undergraduate level in order that students benefit from (1) advanced STEM concepts, (2) project based learning, (3) a systems level perspective and (4) real world applications. This will help students that proceed to graduate school and who take up employment in government or industry. This paper describes a senior level undergraduate course in biometrics, a multidisciplinary area that is highly relevant to society and which has a rapidly growing global market. The course objectives, broad learning outcomes and curricular plan are described. Assessment results based on the analysis of a concept inventory test and student surveys (target versus control group) related to the learning outcomes show that the course was very successful

A Review on Speech Recognition Methods

Voice recognition is the identification of a speaker on the basis of the characteristics of voices. For this, features of speech patterns that differ between individuals are used to achieve the objective. In this paper speaker recognition system are discussed. Implementation of speaker's voice recognition system with MATLAB makes possible use of voice for real life applications. This paper provides a brief review of different DSP based techniques applied for speech recognition

Hospitality Information Systems: Intuitive, Object-Oriented, and Wireless Technology

Automated information system design and implementation is one of the fastest changing aspects of the hospitality industry. During the past several years nothing has increased the professionalism or improved the productivity within the industry more than the application of computer technology. Intuitive software applications, deemed the first step toward making computers more people-literate, object-oriented programming, intended to more accurately model reality, and wireless communications are expected to play a significant role in future technological advancement

Indexing, browsing and searching of digital video

Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver

Deep learning methods in speaker recognition: a review

This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5-6 years. However, as deep learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in speaker recognition too. It seems that DL becomes the now state-of-the-art solution for both speaker verification and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to DL, where they are the most effective

Speaker recognition for door opening systems

Mestrado de dupla diplomação com a UTFPR - Universidade Tecnológica Federal do ParanáBesides being an important communication tool, the voice can also serve for identification purposes since it has an individual signature for each person. Speaker recognition technologies can use this signature as an authentication method to access environments. This work explores the development and testing of machine and deep learning models, specifically the GMM, the VGG-M, and ResNet50 models, for speaker recognition access control to build a system to grant access to CeDRI’s laboratory. The deep learning models were evaluated based on their performance in recognizing speakers from audio samples, emphasizing the Equal Error Rate metric to determine their effectiveness. The models were trained and tested initially in public datasets with 1251 to 6112 speakers and then fine-tuned on private datasets with 32 speakers of CeDri’s laboratory. In this study, we compared the performance of ResNet50, VGGM, and GMM models for speaker verification. After conducting experiments on our private datasets, we found that the ResNet50 model outperformed the other models. It achieved the lowest Equal Error Rate (EER) of 0.7% on the Framed Silence Removed dataset. On the same dataset,« the VGGM model achieved an EER of 5%, and the GMM model achieved an EER of 2.13%. Our best model’s performance was unable to achieve the current state-of-the-art of 2.87% in the VoxCeleb 1 verification dataset. However, our best implementation using ResNet50 achieved an EER of 5.96% while being trained on only a tiny portion of the data than it usually is. So, this result indicates that our model is robust and efficient and provides a significant improvement margin. This thesis provides insights into the capabilities of these models in a real-world application, aiming to deploy the system on a platform for practical use in laboratory access authorization. The results of this study contribute to the field of biometric security by demonstrating the potential of speaker recognition systems in controlled environments.Além de ser uma importante ferramenta de comunicação, a voz também pode servir para fins de identificação, pois possui uma assinatura individual para cada pessoa. As tecnologias de reconhecimento de voz podem usar essa assinatura como um método de autenticação para acessar ambientes. Este trabalho explora o desenvolvimento e teste de modelos de aprendizado de máquina e aprendizado profundo, especificamente os modelos GMM, VGG-M e ResNet50, para controle de acesso de reconhecimento de voz com o objetivo de construir um sistema para conceder acesso ao laboratório do CeDRI. Os modelos de aprendizado profundo foram avaliados com base em seu desempenho no reconhecimento de falantes a partir de amostras de áudio, enfatizando a métrica de Taxa de Erro Igual para determinar sua eficácia. Osmodelos foram inicialmente treinados e testados em conjuntos de dados públicos com 1251 a 6112 falantes e, em seguida, ajustados em conjuntos de dados privados com 32 falantes do laboratório do CeDri. Neste estudo, comparamos o desempenho dos modelos ResNet50, VGGM e GMM para verificação de falantes. Após realizar experimentos em nossos conjuntos de dados privados, descobrimos que o modelo ResNet50 superou os outros modelos. Ele alcançou a menor Taxa de Erro Igual (EER) de 0,7% no conjunto de dados Framed Silence Removed. No mesmo conjunto de dados, o modelo VGGM alcançou uma EER de 5% e o modelo GMM alcançou uma EER de 2,13%. O desempenho do nosso melhor modelo não conseguiu atingir o estado da arte atual de 2,87% no conjunto de dados de verificação VoxCeleb 1. No entanto, nossa melhor implementação usando o ResNet50 alcançou uma EER de 5,96%, mesmo sendo treinado em apenas uma pequena parte dos dados que normalmente são utilizados. Assim, este resultado indica que nosso modelo é robusto e eficiente e oferece uma margem significativa de melhoria. Esta tese oferece insights sobre as capacidades desses modelos em uma aplicação do mundo real, visando implantar o sistema em uma plataforma para uso prático na autorização de acesso ao laboratório. Os resultados deste estudo contribuem para o campo da segurança biométrica ao demonstrar o potencial dos sistemas de reconhecimento de voz em ambientes controlados

Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN

Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a "code-switching" problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin