In this paper, we propose an utterance verification-based postprocessing in online keyword recognition for multimedia content retrieval. The proposed post-processing technique verifies whether a candidate keyword segment can be categorized as a keyword. For this work, we employ a confidence measure based on the recognition results. In keyword recognition experiments, our approach achieved bette...

Cloud computing is a technology that uses the internet and central remote servers to keep up data and applications. As becomes more mature, many organizations and individuals are attracted in storing more accessible data e.g. personal data files, company related information in the cloud. This technology allows for much more efficient computing by centralizing storage, memory, processing and ban...

Keyword Spotting Using Normalization of Posterior Probability Confidence Measures by Rachna Vijay Vargiya Thesis Advisor: Marius C. Silaghi, Ph.D. Keyword spotting techniques deal with recognition of predefined vocabulary keywords from a voice stream. This research uses HMM based keyword spotting algorithms for this purpose. The three most important componenets of a keyword detection system are...

A Public Key Encryption is a technique of encrypting the message using public keys so that the message is secure against various attacks. A Keyword based Public Key Encryption is a new way of providing security against various attacks. Although there are various keyword searches based public key encryption techniques are implemented. Here in this paper a survey of all the techniques based on Ke...

We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm generates a new model for a keyword which takes into account the systematic errors made by the neural network and avoiding those models that can be c...

In this paper, we investigate a new discriminative training technique which focuses on optimizing a keyword error rate, rather than the error rate on all words. We hypothesize that improvements in keyword error rate correlate with improvements in understanding error rates. Keyword-based discriminative training is accomplished by modifying a standard minimum classification error (MCE) training a...

As cloud computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. In this paper, we propose a new method to enable effective fuzzy keyword search in a multi-user system over encrypted clo...

We propose improved Deep Neural Network (DNN) training loss functions for more accurate single keyword spotting on resource-constrained embedded devices. The loss function modifications consist of a combination of multi-task training and weighted cross entropy. In the multi-task architecture, the keyword DNN acoustic model is trained with two tasks in parallel the main task of predicting the ke...

Keyword spotting is an efficient approach for search of relevant recordings in databases of recorded unconstrained speech. Many algorithms have been proposed in the past for this problem and several techniques claim to be very efficient and accurate. Researchers have so far attempted to correctly compare their results by using standardized Receiver Operating Characteristic (ROC) curves, and per...