Speaker Dependent Voice Recognition with Word-Tense Association and Part-of-Speech Tagging

Extensive Research has been conducted on speech recognition and Speaker Recognition over the past few decades. Speaker recognition deals with identifying the speaker from multiple speakers and the ability to filter out the voice of an individual from the background for computational understanding. The more commonly researched method, speech recognition, deals only with computational linguistics. This thesis deals with speaker recognition and natural language processing. The most common speaker recognition systems are Text-Dependent and identify the speaker after a key word/phrase is uttered. This thesis presents Text-Independent Speaker recognition systems that incorporate the collaborative effort and research of noise-filtering, Speech Segmentation, Feature extraction, speaker verification and finally, Partial Language Modelling. The filtering process was accomplished using 4th order Butterworth Band-pass filters to dampen ambient noise outside normal speech frequencies of 300Hzto3000Hz. Speech segmentation utilizes Hamming windows to segment the speech, after which speech detection occurs by calculating the Short time Energy and Zero-crossing rates over a particular time period and identifying voiced from unvoiced using a threshold. Audio data collected from different people is run consecutively through a Speaker Training and Recognition Algorithm which uses neural networks to create a training group and target group for the recognition process. The output of the segmentation module is then processed by the neural network to recognize the speaker. Though not implemented here due to database and computational requirements, the last module suggests a new model for the Part of Speech tagging process that involves a combination of Artificial Neural Networks (ANN) and Hidden Markov Models (HMM) in a series configuration to achieve higher accuracy. This differs from existing research by diverging from the usual single model approach or the creation of hybrid ANN and HMM models

Speaker verification using sequence discriminant support vector machines

This paper presents a text-independent speaker verification system using support vector machines (SVMs) with score-space kernels. Score-space kernels generalize Fisher kernels and are based on underlying generative models such as Gaussian mixture models (GMMs). This approach provides direct discrimination between whole sequences, in contrast with the frame-level approaches at the heart of most current systems. The resultant SVMs have a very high dimensionality since it is related to the number of parameters in the underlying generative model. To address problems that arise in the resultant optimization we introduce a technique called spherical normalization that preconditions the Hessian matrix. We have performed speaker verification experiments using the PolyVar database. The SVM system presented here reduces the relative error rates by 34% compared to a GMM likelihood ratio system