We propose to adapt deep neural network (DNN) acoustic models to a target speaker by
supplying speaker identity vectors (i-vectors) as input features to the network in parallel with …

The processing of speech corrupted by interfering overlap** speakers is one of the
challenging problems with regards to today's automatic speech recognition systems …

Typical speaker verification systems usually employ speakers' audio data collected during
an enrollment phase when users enroll with the system and provide respective voice …

The concept of so called iVectors, where each utterance is represented by fixed-length low-
dimensional feature vector, has recently become very successfully in speaker verification. In …

This paper presents a novel fully automatic bi-modal, face and speaker, recognition system
which runs in real-time on a mobile phone. The implemented system runs in real-time on a …

This paper presents the application of Neural Network Bottleneck (BN) features in Language
Identification (LID). BN features are generally used for Large Vocabulary Speech …

Mobile Biometrics (MoBio): Joint Face and Voice Verification for a Mobile Platform Page 1
Mobile Biometrics (MoBio): Joint Face and Voice Verification for a Mobile Platform PA …

ALIZE is an open-source platform for speaker recognition. The ALIZE library implements a
low-level statistical engine based on the well-known Gaussian mixture modelling. The toolkit …

In the case c= 1 and Nc= 1 this is the same as the PPCA posterior calculation (Bishop)
Accumulate the matrix∑ c NcV∗ c Vc for each utterance The matrices V∗ c Vc are typically …

We investigate the concept of speaker adaptive training (SAT) in the context of deep neural
network (DNN) acoustic models. Previous studies have shown success of performing …