Discriminative Tandem Features for HMM-based EEG Classification

Abstract—We investigate the use of discriminative feature extractors in tandem configuration with generative EEG classification system. Existing studies on dynamic EEG classification typically use hidden Markov models (HMMs) which lack discriminative capability. In this paper, a linear and a non-linear classifier are discriminatively trained to produce complementary input features to the conventional HMM system. Two sets of tandem features are derived from linear discriminant analysis (LDA) projection output and multilayer perceptron (MLP) class-posterior probability, before appended to the standard autoregressive (AR) features. Evaluation on a two-class motor-imagery classification task shows that both the proposed tandem features yield consistent gains over the AR baseline, resulting in significant relative improvement of 6.2% and 11.2 % for the LDA and MLP features respectively. We also explore portability of these features across different subjects. Index Terms- Artificial neural network-hidden Markov models, EEG classification, brain-computer-interface (BCI)

Impact of deep MLP architecture on different acoustic modeling techniques for under-resourced speech recognition

Posterior based acoustic modeling techniques such as Kullback– Leibler divergence based HMM (KL-HMM) and Tandem are able to exploit out-of-language data through posterior fea-tures, estimated by a Multi-Layer Perceptron (MLP). In this paper, we investigate the performance of posterior based ap-proaches in the context of under-resourced speech recognition when a standard three-layer MLP is replaced by a deeper five-layer MLP. The deeper MLP architecture yields similar gains of about 15 % (relative) for Tandem, KL-HMM as well as for a hybrid HMM/MLP system that directly uses the poste-rior estimates as emission probabilities. The best performing system, a bilingual KL-HMM based on a deep MLP, jointly trained on Afrikaans and Dutch data, performs 13 % better than a hybrid system using the same bilingual MLP and 26% better than a subspace Gaussian mixture system only trained on Afrikaans data. Index Terms — KL-HMM, Tandem, hybrid system, deep MLPs, under-resourced speech recognitio

Deep neural network features and semi-supervised training for low resource speech recognition

We propose a new technique for training deep neural networks (DNNs) as data-driven feature front-ends for large vocabulary con-tinuous speech recognition (LVCSR) in low resource settings. To circumvent the lack of sufficient training data for acoustic mod-eling in these scenarios, we use transcribed multilingual data and semi-supervised training to build the proposed feature front-ends. In our experiments, the proposed features provide an absolute im-provement of 16 % in a low-resource LVCSR setting with only one hour of in-domain training data. While close to three-fourths of these gains come from DNN-based features, the remaining are from semi-supervised training. Index Terms — Low resource, speech recognition, deep neural networks, semi-supervised training, bottleneck features

The RWTH Aachen German and English LVCSR systems for IWSLT-2013

Abstract In this paper, German and English large vocabulary continuous speech recognition (LVCSR) systems developed by the RWTH Aachen University for the IWSLT-2013 evaluation campaign are presented. Good improvements are obtained with state-of-the-art monolingual and multilingual bottleneck features. In addition, an open vocabulary approach using morphemic sub-lexical units is investigated along with the language model adaptation for the German LVCSR. For both the languages, competitive WERs are achieved using system combination