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EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding

The performance of automatic speech recognition (ASR) has improved tremendously due to the application of deep neural networks (DNNs). Despite this progress, building a new ASR system remains a challenging task, requiring various resources, multiple training stages and significant expertise. This paper presents our Eesen framework which drastically simplifies the existing pipeline to build state-of-the-art ASR systems. Acoustic modeling in Eesen involves learning a single recurrent neural network (RNN) predicting context-independent targets (phonemes or characters). To remove the need for pre-generated frame labels, we adopt the connectionist temporal classification (CTC) objective function to infer the alignments between speech and label sequences. A distinctive feature of Eesen is a generalized decoding approach based on weighted finite-state transducers (WFSTs), which enables the efficient incorporation of lexicons and language models into CTC decoding. Experiments show that compared with the standard hybrid DNN systems, Eesen achieves comparable word error rates (WERs), while at the same time speeding up decoding significantly.

1507.08240v3 EESEN End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding.pdf

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Makale: VoxNet: A 3D Convolutional Neural Network for Real-Time Object Recognition

Robust  object  recognition  is  a  crucial  skill  for robots  operating  autonomously  in  real  world  environments. Range  sensors  such  as  LiDAR  and  RGBD  cameras  are  increasingly  found  in  modern  robotic  systems,  providing  a  rich source  of  3D  information  that  can  aid  in  this  task.  However, many current systems do not fully utilize this information and have  trouble  efficiently  dealing  with  large  amounts  of  point cloud  data.  In  this  paper,  we  propose VoxNet,  an  architecture to  tackle  this  problem  by  integrating  a  volumetric  Occupancy Grid representation with a supervised 3D Convolutional Neural Network  (3D  CNN).  We  evaluate  our  approach  on  publicly available  benchmarks  using  LiDAR,  RGBD,  and  CAD  data.
VoxNet achieves  accuracy  beyond  the  state  of  the  art  while labeling hundreds of instances per second.

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