Thesis speaker verification

To show safety, it suffices if these assertions are approximate enough to be 'program invariants'. Therefore, it is impossible to V. An endorsement letter by the department head or a suitable substitute in case of conflict of interest.

On the other hand the K loop is not parallelized for the fact that the inner loop T is big enough to cover the cost of thread activation. In our implementation the iterations are repeated 50 times.

This however, will not change the computation complexity of EM algorithm. For example in the case of initializing sum of means and sum of variances we will only need D threads while for updating them we need T threads. The scale is divided into the units mel.

As a reference point, the pitch of a 1 KHz tone, 40dB above the perceptual hearing threshold, is defined as mels. The Mel scale is mainly based on the study of observing the pitch or frequency perceived by the human. This is the main target architecture in this thesis. Larynx refers as an energy signal Three main components shown in this structure are: Therefore, we focus on explaining those common modules and later in chapter 6 we will illustrate the results separately for each step of SV.

Year - Tomi Kinnunen writes his MSc thesis about the subject with the knowledge virtually nothing about acoustics, phonetics or DSP - but very enthusiastic about general pattern recognition.

The Mel scale filter bank is a series of l triangular band pass filters that have been designed to simulate the band pass filtering believed to occur in the auditory system. Further development is currently under work.

The real cepstrum uses the information of the magnitude of the spectrum where as complex cepstrum holds information about both magnitude and phase of the initial spectrum, which allows the reconstruction of the signal. Hence, this approach helps in improving the efficiency of the system.

When a speaker needs to be verified, then the distortion distance is calculated for varying number of filters like in the above order and minimum distance obtained against a speaker happens to match the claimed identity, then the speaker is verified.

It constructs a new partition by associating each point with the closest centroid. In OVA training settings, we show that the AMC-SVM can, under certain conditions, be formulated to yield a single, fixed kernel function that applies universally to any choice of target speaker. Utilizing the bank filter is much more convenient to do Mel frequency warping, with filters centred according to Mel frequency.

On GT architecture they achieved the speedup of compared to CPU single-threaded implementation. Speaker verification or models created by using training utterances for the Speaker verification SV is the process of determining passwords. Supporting letters should not come directly from the supporters—all necessary material, including supporting letters, may be collected and submitted by the thesis advisor, bundled as a zip file.

On CUDA architecture, the problems are divided into sub-problems and each subproblem into finer pieces that can cooperatively run in parallel by all threads within the block.

The vocal tract speech can be characterized in terms of the signal carrying works as a filter to shape the excitation sources. The program is in test use at the University of Helsinki, Department of Phonetics. Home shopping see for e.

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One copy of the thesis in electronic format A record of publications in conferences and journals of the work reported in the dissertation, along with their citations, if any. The CPU has 4 cores 8 hyper-threads running at 2.

According to the Mel frequency the width of the triangular filters vary and so the log total energy in a critical band around the centre frequency is included. Wait until coded, tested and reported. This software is able to recognize the identity of a client previously trained in a database, and works independently of the text spoken.

This website does not use this type of cookie. We will discuss the importance of grid size and block size in Chapter 6. Additionally, random reads and writes on the memory were not possible because of architectural limitations.

Parallel For t between 0 and T. This thesis explores the use of Bayesian distance metric learning (Bayes_dml) for the task of speaker verification using the i-vector feature representation.

Speaker recognition

We propose a framework that explores the distance constraints between i-vector pairs from the same speaker and different speakers. Speaker Verification in JAVA October 18, 4 Preface This thesis is a part of my education towards a Master degree in Computer and Information Engineering at Griffith University, Brisbane, Australia.

I completed my PhD thesis in April and it was selected to be published in the Springer best thesis series. My research on privacy-preserving speaker verification received significant press coverage. The objective of this thesis is to develop automatic text-independent speaker verification systems using unconstrained telephone conversational speech.

We began by performing a Gaussian Mixture Model Likelihood ratio verification task in speaker independent system as described by MIT Lincoln Lab.

The thesis research examines the emergence of surveillance and biometrics technologies as a sensible baseline for building a ubiquitous surveillance testbed for the Naval Postgraduate School. This thesis examines the suitability of nasal resonance patterns as a means of authenticating speakers' identities in an automatic speaker verification system.

Thesis speaker verification
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