Ruben Zazo

  • Citations Per Year
Learn More
Long Short Term Memory (LSTM) Recurrent Neural Networks (RNNs) have recently outperformed other state-of-the-art approaches, such as i-vector and Deep Neural Networks (DNNs), in automatic Language Identification (LID), particularly when dealing with very short utterances (∼3s). In this contribution we present an open-source, end-to-end, LSTM RNN system(More)
Long Short-Term Memory recurrent neural networks (LSTM RNNs) provide an outstanding performance in language identification (LID) due to its ability to model speech sequences. So far, previously published LSTM RNNs solutions for LID deal with highly controlled scenarios, balanced datasets and limited channel variability. In this paper we evaluate an(More)
Language recognition systems based on bottleneck features have recently become the state-of-the-art in this research field, showing its success in the last Language Recognition Evaluation (LRE 2015) organized by NIST (U.S. National Institute of Standards and Technology). This type of system is based on a deep neural network (DNN) trained to discriminate(More)
  • 1