Speech recognition is the task of recognising speech within audio and converting it into text.
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Because of its streaming nature, recurrent neural network transducer (RNN-T) is a very promising end-to-end (E2E) model that may replace the popular hybrid model for automatic speech recognition.
The first problem concerns unsupervised discovery of basic (subword level) speech units in a given language.
In the context of wireless networking, it was recently shown that multiple DNNs can be jointly trained to offer a desired collaborative behaviour capable of coping with a broad range of sensing uncertainties.
Unlike previous work on this topic, which performs on-the-fly limited-size beam-search decoding and generates alignment scores for expected edit-distance computation, in our proposed method, we re-calculate and sum scores of all the possible alignments for each hypothesis in N-best lists.
To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems.
In recent years, deep learning has made great progress in the fields of image recognition, video analysis, natural language processing and speech recognition, including video super-resolution tasks.
To address various shortcomings of this paradigm, recent work explores end-to-end trainable direct models that translate without transcribing.
This aspect remains in stark contrast to learning with error backpropagation through time (BPTT) applied to recurrent neural networks (RNNs), or recently even to biologically-inspired spiking neural networks (SNNs), because the unrolling through time of BPTT leads to system-locking problems.
Recurrent Neural Network Language Models (RNNLMs) have started to be used in various fields of speech recognition due to their outstanding performance.
Each sliced window is classified to a label at the corresponding time through iterative routing mechanisms.