Development of End-to-End Models: Difference between revisions
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The development of End-to-End (E2E) models represent a significant shift in the field of automatic speech recognition (ASR), which seek to simplify the complex pipeline of traditional systems by directly mapping input audio sequence to sequence of words or other graphemes.<ref>Wang, Dong, Xiaodong Wang, and Shaohe Lv. 2019. “An Overview of End-to-End Automatic Speech Recognition.” ''Symmetry'' 11(8):1018.</ref> Framed in the [[Deep Learning Revolution|Deep Learning]] context and taking advantage of Neural Network(NN) architectures, these models directly capture the acoustic and linguistic information present in the speech signal, casting a possibly complex processing pipeline into the coherent and flexible modeling language of neural networks.<ref>Glasmachers, Tobias. “Limits of end-to-end learning.”arXiv preprint arXiv:1704.08305 (2017).</ref> The functional structure of E2E models is shown below<blockquote> | The development of End-to-End (E2E) models represent a significant shift in the field of automatic speech recognition (ASR), which seek to simplify the complex pipeline of traditional systems by directly mapping input audio sequence to sequence of words or other graphemes.<ref>Wang, Dong, Xiaodong Wang, and Shaohe Lv. 2019. “An Overview of End-to-End Automatic Speech Recognition.” ''Symmetry'' 11(8):1018.</ref> Framed in the [[Deep Learning Revolution|Deep Learning]] context and taking advantage of Neural Network(NN) architectures, these models directly capture the acoustic and linguistic information present in the speech signal, casting a possibly complex processing pipeline into the coherent and flexible modeling language of neural networks.<ref>Glasmachers, Tobias. “Limits of end-to-end learning.”arXiv preprint arXiv:1704.08305 (2017).</ref> The functional structure of E2E models is shown below<blockquote> | ||
====== '''''L'' = {<math>l_1</math>,···，<math>l_n</math>}''' ''<small>output sequence</small>'' ====== | ====== '''''L'' = {<math>l_1</math>,···，<math>l_n</math>}''' ''<small>output sequence</small>'' ====== | ||
<math>\uparrow</math> | <math>\uparrow</math> | ||
'''Decoder''' | '''Decoder''' | ||
Revision as of 17:26, 16 September 2023
YiningLei, Xinyi Ma, Liqing, Jingwen Shi
Introduction
The development of End-to-End (E2E) models represent a significant shift in the field of automatic speech recognition (ASR), which seek to simplify the complex pipeline of traditional systems by directly mapping input audio sequence to sequence of words or other graphemes.[1] Framed in the Deep Learning context and taking advantage of Neural Network(NN) architectures, these models directly capture the acoustic and linguistic information present in the speech signal, casting a possibly complex processing pipeline into the coherent and flexible modeling language of neural networks.[2] The functional structure of E2E models is shown below
L = {,···，} output sequence
Decoder
Aligner
F = {,···，} feature sequence
Encoder
X = {,···，} input sequence
Historical Context
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Key Innovations
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Impact
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Future research
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References
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- ↑ Wang, Dong, Xiaodong Wang, and Shaohe Lv. 2019. “An Overview of End-to-End Automatic Speech Recognition.” Symmetry 11(8):1018.
- ↑ Glasmachers, Tobias. “Limits of end-to-end learning.”arXiv preprint arXiv:1704.08305 (2017).
- ↑ Feng, S., Kudina, O., Halpern, B. M., & Scharenborg, O. (2021). Quantifying Bias in Automatic Speech Recognition (arXiv:2103.15122). arXiv. http://arxiv.org/abs/2103.15122
- ↑ Glantz, Richard "SHOEBOX: a personal file handling system for textual data." In Proceedings of the November 17-19, 1970, Fall Joint Computer Conference 1970. 535-545. [1]
<ref>