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== Impact == ==== 1.The Impact on the Field of Speech Recognition ==== ===== Simplify Processes and Reduce Labor Costs: ===== The end-to-end model eliminates the need for multiple processing steps and manual feature engineering, reducing the time and effort to develop and maintain the model. In the aspect of speech recognition, the end-to-end model simplifies the architecture of the speech recognition system, eliminating the need for complex hand-designed feature extractors or acoustic models. ===== Better Performance: ===== End-to-end models can extract relevant features directly from the original data, sometimes better than traditional processes to capture the complex relationships of the data, so the performance of the model may be improved. The end-to-end model can better capture the complex relationship between audio and text, which usually enables speech recognition to achieve higher recognition accuracy than traditional methods. ===== More Extensive Applications: ===== The end-to-end model can be applied to a variety of tasks, such as speech recognition, machine translation, image processing and so on, making the solutions of these tasks easier to implement and popularize. For speech recognition, this simplified method can be easily applied to a variety of speech recognition tasks, including multiple languages, accents from all over the world, language habits of various cultural backgrounds and speech recognition in specific fields. ===== Faster Experimental Iteration: ===== The simplified modeling process enables researchers to try new ideas and algorithms faster and faster, accelerating research progress. The end-to-end model provides more flexibility and innovative possibilities for researchers in the field of speech recognition. ===== More Efficient Training: ===== End-to-end models may require more data to train, and more computer resources to process and train these large models. But at the same time, end-to-end training can provide more efficient training for speech recognition, and direct training from original audio to text can make more efficient use of a large number of tagged data and improve the performance of the model. ===== Listen, Attend and Spell (LAS) ===== Listen, Attend and Spell (LAS), is an attention-based neural network that can di-rectly transcribe acoustic signals to characters. LAS is based on the sequence to sequence framework with a pyramid structure in the encoder that reduces the number of timesteps that the decoder has to attend to. LAS is trained end-to-end and has two main components: a listener and a speller. The listener is a pyramidal recurrent net- work encoder that accepts filter bank spectra as inputs. The speller is an attention- based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions be- tween the characters. This is the key improvement of LAS over previous end-to-end [[wikipedia:Connectionist_temporal_classification|CTC models]]. On a subset of the Google voice search task, LAS achieves a word error rate (WER) of 14.1% without a dictionary or a language model, and 10.3% with language model rescoring over the top 32 beams. By comparison, the state-of-the-art [https://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/43455.pdf CLDNN]-HMM model achieves a WER of 8.0%.<ref>Chan, William, Navdeep Jaitly, Quoc V. Le和Oriol Vinyals. "Listen, Attend and Spell". arXiv, 19.08.2015. http://arxiv.org/abs/1508.01211.</ref> ==== 2.The Impact of Its Applications in Various Industries ==== ===== Transformer ===== Transformer, is the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks<ref>Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, and Rob Fergus. End-to-end memory networks. In C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, and R. Garnett, editors, Advances in Neural Information Processing Systems 28, pages 2440–2448. Curran Associates, Inc., 2015.https://arxiv.org/abs/1503.08895</ref>. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks our best model outer forms even all previously reported ensembles.<ref>Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser和Illia Polosukhin. "Attention Is All You Need". arXiv, 01.08.2023. http://arxiv.org/abs/1706.03762.</ref> ===== Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks ===== Region Proposal Network ([https://paperswithcode.com/method/rpn RPN]) shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast [https://towardsdatascience.com/r-cnn-fast-r-cnn-faster-r-cnn-yolo-object-detection-algorithms-36d53571365e R-CNN] for detection. Adaptively-sized pooling (SPP) on shared convolutional feature maps is developed for efficient region-based object detection<ref>K. He, X. Zhang, S. Ren, and J. Sun, “Spatial pyramid pooling in deep convolutional networks for visual recognition,” in European Conference on Computer Vision (ECCV), 2014.https://arxiv.org/abs/1406.4729</ref>, and semantic segmentation. Fast R-CNN enables end-to-end detector training on shared convolutional features and shows compelling accuracy and speed.<ref>Ren, Shaoqing, Kaiming He, Ross Girshick和Jian Sun. "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks". arXiv, 06.01.2016. http://arxiv.org/abs/1506.01497.</ref>
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