Phoneme Based Speaker Verification System Based on Two Stage Self-Organizing Map Design

Speaker verification is one of the pattern recognition task that authenticate a person by his or her voice. This thesis deals with a relatively new technique of classification that is the self-organizing map (SOM). Self-organizing map, as an unsupervised learning artificial neural network, rarely used as final classification step in pattern recognition task due to its relatively low accuracy. A two-stage self-organizing map design has been implemented in this thesis and showed improved results over conventional single stage design. For speech features extraction, this thesis does not introduce any new technique. A well study method that is the linear prediction analysis (LP A) has been used. Linear predictive analysis derived coefficients are extracted from segmented raw speech signal to train and test the front stage self-organizing map. Unlike other multistage or hierarchical self-organizing map designs, this thesis utilized residual vectors generated from front stage self-organizing map to train and test the second stage selforganizing map. The results showed that by breaking the classification tasks into two level or more detail resolution, an improvement of more than 5% can be obtained. Moreover, the computation time is also reduced greatly

Voice Conversion Using Sequence-to-Sequence Learning of Context Posterior Probabilities

Voice conversion (VC) using sequence-to-sequence learning of context posterior probabilities is proposed. Conventional VC using shared context posterior probabilities predicts target speech parameters from the context posterior probabilities estimated from the source speech parameters. Although conventional VC can be built from non-parallel data, it is difficult to convert speaker individuality such as phonetic property and speaking rate contained in the posterior probabilities because the source posterior probabilities are directly used for predicting target speech parameters. In this work, we assume that the training data partly include parallel speech data and propose sequence-to-sequence learning between the source and target posterior probabilities. The conversion models perform non-linear and variable-length transformation from the source probability sequence to the target one. Further, we propose a joint training algorithm for the modules. In contrast to conventional VC, which separately trains the speech recognition that estimates posterior probabilities and the speech synthesis that predicts target speech parameters, our proposed method jointly trains these modules along with the proposed probability conversion modules. Experimental results demonstrate that our approach outperforms the conventional VC.Comment: Accepted to INTERSPEECH 201

The Microsoft 2016 Conversational Speech Recognition System

We describe Microsoft's conversational speech recognition system, in which we combine recent developments in neural-network-based acoustic and language modeling to advance the state of the art on the Switchboard recognition task. Inspired by machine learning ensemble techniques, the system uses a range of convolutional and recurrent neural networks. I-vector modeling and lattice-free MMI training provide significant gains for all acoustic model architectures. Language model rescoring with multiple forward and backward running RNNLMs, and word posterior-based system combination provide a 20% boost. The best single system uses a ResNet architecture acoustic model with RNNLM rescoring, and achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The combined system has an error rate of 6.2%, representing an improvement over previously reported results on this benchmark task