4 accepted papers
In this work, we propose contextual language models that incorporate dialog level discourse information into language modeling. Previous works on contextual language model treat preceding utterances as a sequence of inputs, without considering dialog interactions. We design recurrent neural network…
In our previous work, we developed a GPU-accelerated speech recognition engine optimized for faster than real time speech recognition on a heterogeneous CPU-GPU architecture. In this work, we focused on developing a scalable server-client architecture specifically optimized to simultaneously decode…
Convolutional Neural Networks (CNNs) have demonstrated powerful acoustic modelling capabilities due to their ability to account for structural locality in the feature space; and in recent works CNNs have been shown to often outperform fully connected Deep Neural Networks (DNNs) on TIMIT and LVCSR. I…
In particular for “low resource” Keyword Search (KWS) and Speech-to-Text (STT) tasks, more untranscribed test data may be available than training data. Several approaches have been proposed to make this data useful during system development, even when initial systems have Word Error Rates (WER) abov…