Speaker Embedding Extraction with Virtual Phonetic Information In the recent past, deep neural networks have been successfully employed to extract fixeddimensional speaker embeddings from the speech signal. The commonly used xvectors are extracted by projecting the magnitude spectral features extracted from the speech signal onto a speakerdiscriminative space. As the xvectors do not explicitly capture the speakerspecific phonological pronunciation variability, phonetic vectors extracted from an automatic speech recognition (ASR) engine were supplied as auxiliary information to improve the performance of the xvector system. However, the development of ASR engine requires a huge amount of manually transcribed speech data. In this paper, we propose to transcribe the speech signal in an unsupervised manner with the cluster labels obtained from a mixture of autoencoders (MoA) trained on a large amount of speech data. The unsupervised labels, referred to as virtual phonetic transcriptions, are used to extract the phonetic vectors. The virtual phonetic vectors extracted using MoA are supplied as auxiliary information to the xvector system. The performance of the proposed system is compared with the stateoftheart xvector system on NIST SRE2010 data. The proposed unsupervised auxiliary information provides a relative improvement of 12.08%, 3.61% and 16.66% over the xvector system on corecore, core10sec and 10sec10sec conditions, respectively.

Investigating Domain Sensitivity of DNN Embeddings for Speaker Recognition Systems A speaker embeddings framework achieves stateoftheart speaker recognition performance by modeling speaker discriminant information directly using deep neural networks (DNNs). After the introduction of neural network based speaker embeddings, researchers have explored the requirements for training an effective embeddings network. However, the domain of the data used for system development should match the domain of operation for optimal performance. In this paper, we investigate the sensitivity of domain mismatch in the embeddings space. Specifically, degradation in performance is observed when backend scoring with embeddings is performed with outdomain data. To compensate for the domain mismatch, we propose two novel deep domain adaptation techniques based on autoencoder architectures trained on embeddings in an unsupervised fashion. The results show that domain mismatch can be compensated effectively using autoencoders to adapt the outdomain data to indomain.