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The Conformer is a speech-recognition encoder architecture combining self-attention for global context with convolution for local patterns in an audio sequence.
Déglul tegtal biiIA-nettali · ~1 min
The Conformer paper proposes a convolution-augmented Transformer for automatic speech recognition. Its block combines multi-head self-attention, a convolution module and two half-step feed-forward modules in a Macaron-style structure. Relative positional encoding supports attention over sequence positions. The authors evaluate on LibriSpeech and report results under specific model sizes and decoding conditions.
Self-attention models global interactions across speech frames.
A convolution module captures local patterns in the encoded sequence.
Macaron-style blocks place feed-forward modules around attention and convolution sublayers.
The original paper reports LibriSpeech word error rates for models with and without an external language model.
Compare Conformer variants on the target audio conditions and compute budget. Report model size, decoding procedure and external language-model use. The original paper does not establish that Conformer is the dominant architecture across every production speech system.
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The Conformer is a speech-recognition encoder architecture combining self-attention for global context with convolution for local patterns in an audio sequence.
Conformer dafay boole convolution (ngir motif local) ak bàyyi xel ci sa bopp (ngir contexte global) ci biir bloc bu nekk.
Convolutions yi dañu gëna am solo ci siñaal yu ndaw yiy wuutale fonem yi, fekk bàyyi xel ci sa bopp dafay wane dependence ci wax ji yépp; Konformer amna ñoom ñaar.
Conformer dafay def module yiy bàyyi xel ci seen bopp ak yiy convolution ci diggante ñaari module feed-forward, bu nekk ci ñoom def ci residu bu am genn-wàll pondéré.
Gëstukat yi Google ñoo dugal Conformer ci 2020 ci diir bu gàtt mu nekk enkodeer buy xàmmee kàddu yiñ miin.
Module konvolusioŋ dafay boole convolusioŋ pointwise ak benn unité ligneaire buñ tëj, convolusioŋ xóotaayu, normalisasioŋ lots, ak aktivasioŋ Swish, mujjee ci beneen conv pointwise.
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Ci bi mu melGis bi ci topp
Architecture de parole
Audio IA