In this paper, we introduce a new approach to dialect recognition which relies on the hypothesis that certain phones are realized differently across dialects. Given a speaker’s utterance, we first obtain the most likely phone sequence using a phone recognizer. We then extract GMM Supervectors for each phone instance. Using these vectors, we design a kernel function that computes the similaritie...

This paper describes a new speaker verification system based on orthogonal Gaussian mixture modeling (GMM) techniques combined with maximum a posteriori (MAP) adaptation. In most of the GMM based speaker verification systems, the variance of each component is constrained to be diagonal for its computational simplicity. However, this approximation inevitably introduces performance degradation. T...

This paper presents results on age-group identification (AgeID) for children’s speech, using the OGI Kids corpus and GMM-UBM, GMM-SVM and i-vector systems. Regions of the spectrum containing important age information for children are identified by conducting Age-ID experiments over 21 frequency sub-bands. Results show that the frequencies above 5.5 kHz are least useful for Age-ID. The effect of...

We use a multi-layer perceptron (MLP) to transform cepstral features into features better suited for speaker recognition. Two types of MLP output targets are considered: phones (Tandem/HATS-MLP) and speakers (Speaker-MLP). In the former case, output activations are used as features in a GMM speaker recognition system, while for the latter, hidden activations are used as features in an SVM syste...

This paper investigates a method for training bottleneck (BN) features in a more targeted manner for their intended use in GMM-HMM based ASR. Our approach adds a GMM acoustic model activation layer to a standard BN feature extraction (FE) neural network and performs lattice-based MMI training on the resulting network. After training, the network is reverted back into a working BN FE network by ...

This paper introduces and motivates the use of the statistical method Gaussian Mixture Model (GMM) and Support Vector Machines (SVM) for robust textindependent speaker identification. Features are extracted from the dialect DR1 of the Timit corpus. They are presented by MFCC, energy, Delta and Delta-Delta coefficients. GMM is used to model the feature extractor of the input speech signal and SV...

Two approaches to detecting and tracking speakers in multispeaker audio are described. Both approaches use an adapted Gaussian mixture model, universal background model (GMM-UBM) speaker detection system as the core speaker recognition engine. In one approach, the individual log-likelihood ratio scores, which are produced on a frame-by-frame basis by the GMM-UBM system, are used to first partit...

Our previous experiments in Text-Dependent and -Independent Speaker Verification (TD-SV and TI-SV) using trajectory-based models, showed that non-stationary segments benefit TD-SV but not TI-SV, because in TI-SV maximum likelihood (ML) training results mainly in stationary segments. This result questions the role of non-stationary, ‘delta’ parameters in conventional GMM-based TI-SV. In this pap...

We have previously developed a one-to-many eigenvoice conversion (EVC) system enabling the conversion from a specific source speaker’s voice into an arbitrary target speaker’s voice. In this system, eigenvoice Gaussian mixture model (EV-GMM) is trained in advance with multiple parallel data sets composed of utterance pairs of the source and many pre-stored target speakers. The EV-GMM is effecti...