6 accepted papers
Max-pooling neural network architectures have been proven to be useful for keyword spotting (KWS), but standard training methods suffer from a class-imbalance problem when using all frames from negative utterances. To address the problem, we propose an innovative algorithm, Regional Hard-Example (RH…
Long Short Term Memory Connectionist Temporal Classification (LSTM-CTC) based end-to-end models are widely used in speech recognition due to its simplicity in training and efficiency in decoding. In conventional LSTM-CTC based models, a bottleneck projection matrix maps the hidden feature vectors ob…
Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurrent Neural Network language model (RNNLM) or an ensemble of several RNNLMs is used. These models have large memory footpri…
In this paper, we propose a domain adversarial training (DAT) algorithm to alleviate the accented speech recognition problem. In order to reduce the mismatch between labeled source domain data (“standard” accent) and unlabeled target domain data (with heavy accents), we augment the learning objectiv…
In many spoken language understanding systems (SLUS), domain classification is the most crucial component, as system responses based on wrong domains often yield very unpleasant user experiences. In multi-lingual domain classification, the training data for some poor-resource languages often comes f…
We present a contextual spoken language understanding (contextual SLU) method using Recurrent Neural Networks (RNNs). Previous work has shown that context information, specifically the previously estimated domain assignment, is helpful for domain identification. We further show that other context in…