Deep Normalization for Speaker Vectors

Deep Speaker Vectors for Semi Text-independent Speaker Verification

Recent research shows that deep neural networks (DNNs) can be used to extract deep speaker vectors (d-vectors) that preserve speaker characteristics and can be used in speaker verification. This new method has been tested on text-dependent speaker verification tasks, and improvement was reported when combined with the conventional i-vector method. This paper extends the d-vector approach to sem...

Source normalization for language-independent speaker recognition using i-vectors

Source-normalization (SN) is an effective means of improving the robustness of i-vector-based speaker recognition for under-resourced and unseen cross-speech-source evaluation conditions. The technique of source-normalization estimates directions of undesired within-speaker variation more accurately than traditional methods when cross-source variation is not explicitly observed from each speake...

Speaker normalization using HMM2

Improved Speaker Markov Modelling for Unsupervised Speaker Normalization

We propose new methods of improved speech recognition with speaker-variable Information. Hidden Markov Model-based recognizers which are trained by reference speaker(s) (RS) are normalized by our two different approaches to give a better speaker-independent recognition rate. Our normalization methods are based on the same principle of inter-speaker Markov mapping. This mapping gives inter-speak...

Speaker independent acoustic modeling using speaker normalization

This paper proposes a novel speaker-independent (SI) modeling for spontaneous speech data from multiple speakers. The SI acoustic model parameters are estimated by individual training for inter-speaker variability and for intraspeaker phonetically related variation in order to obtain a more accurate acoustic model. The linear transformation technique is used for the speaker normalization to ext...