In this work, we conducted an empirical comparative study of the performance text-independent speaker verification in emotional and stressful environments. This work combined deep models with shallow architecture, which resulted novel hybrid classifiers. Four distinct were utilized: neural network-hidden Markov model (DNN-HMM), network-Gaussian mixture (DNN-GMM), Gaussian model-deep network (GM...

In this paper, we propose a novel method of normalizing the voice quality in an utterance for both clean speech and speech contaminated by noise. The normalization method is applied to the N-best hypotheses from an HMM-based classifier, then an SM (Sub-space Method)-based verifier tests the hypotheses after normalizing the monophone scores together with the HMMbased likelihood score. The HMM-SM...

We present a novel automatic speech recognition (ASR) scheme which uses the recently proposed noise robust exemplar matching framework for speech enhancement in the front-end. The proposed system employs a GMM-HMM back-end to recognize the enhanced speech signals unlike the prior work focusing on template matching only. Speech enhancement is achieved using multiple dictionaries containing speec...

A significant extension to a novel inventory based speech processing procedure published by the authors in 2009 and 2010 is presented [1, 2]. The method is based on a speech analysis and re-synthesis scheme for scenarios in which speaker enrollment and noise enrollment are feasible. The procedure jointly provides speech enhancement and high-quality low-rate speech encoding with a flexible rate ...

We describe a hidden Markov model (HMM)-based speech synthesis system developed at the Nagoya Institute of Technology (NIT) for Blizzard Challenge 2009. We incorporated several state-of-the-art technologies into this system, including the Speech Transformation and Representation using Adaptive Interpolation of weiGHTed spectrum (STRAIGHT) vocoder, minimum generation error (MGE) training, phone ...

In the present paper, a trajectory model, derived from the hidden Markov model (HMM) by imposing explicit relationships between static and dynamic feature vector sequences, is developed and evaluated. The derived model, named trajectory HMM, can alleviate some limitations of the standard HMM, which are i) piece-wise constant statistics within a state and ii) conditional independence assumption ...

Most of the current state-of-the-art speech recognition systems are based on HMMs which usually use mixture of Gaussian functions as state probability distribution model. It is a common practice to use EM algorithm for Gaussian mixture parameter learning. In this case, the learning is done in a ”blind”, data-driven way without taking into account how the speech signal has been produced and whic...

In this paper we compare two different methods for phonetically labeling a speech database. The first approach is based on the alignment of the speech signal on a high quality synthetic speech pattern, and the second one uses a hybrid HMM/ANN system. Both systems have been evaluated on French read utterances from a speaker never seen in the training stage of the HMM/ANN system and manually segm...