The multiple-hypothesis approach yields a relative reduction of 3. 3% WER on the CHiME-4's single-channel real noisy evaluation set when compared with the single-hypothesis approach.
This paper proposes an efficient and robust algorithm to estimate target trajectories with unknown target detection profiles and clutter rates using measurements from multiple sensors.
In this method, multiple automatic speech recognition (ASR) 1-best hypotheses are integrated in the computation of the connectionist temporal classification (CTC) loss function.
Although the lower layers of a deep neural network learn features which are transferable across datasets, these layers are not transferable within the same dataset.
The challenges in multi-object tracking mainly stem from the random variations in the cardinality and states of objects during the tracking process.
Interpreting the top layers as a classifier and the lower layers a feature extractor, one can hypothesize that unwanted network convergence may occur when the classifier has overfit with respect to the feature extractor.
On WSJ corpus, the relative reduction of word error rate (WER) yielded by high-frame-rate features extraction independently and in combination with speed perturbation are up to 21. 3% and 24. 1%, respectively.