Discover and read the best of Twitter Threads about #ICASSP2020
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Super-excited about our new #ICASSP2020 paper on "Universal Phone Recognition with a Multilingual Allophone System" arxiv.org/abs/2002.11800
We create a multi-lingual ASR model that can do zero-shot phone recognition in up to 2,186 languages! How? A little linguistics :) 1/5
We create a multi-lingual ASR model that can do zero-shot phone recognition in up to 2,186 languages! How? A little linguistics :) 1/5
In our speech there are phonemes (sounds that can support lexical contrasts in a *particular* language) and their corresponding phones (the sounds that are actually spoken, which are language *independent*). Most multilingual ASR models conflate these two concepts. 2/5 
We create a model that first recognizes to language-independent phones, and then converts these phones to language-specific phonemes. This makes our underlying representations of phones more universal and generalizable across languages. 3/5
I am thrilled to announce our paper “Feedback Recurrent AutoEncoder” was accepted at #ICASSP2020! collaboration with Yang Yang, @TacoCohen and Jon Ryu. arxiv.org/abs/1911.04018. A quick thread.
In this paper we present a simple yet powerful idea: when using a recurrent AE to perform online lossy compression of a highly temporally correlated signal, one should feedback the state of the decoder to the encoder. We compare FRAE to many natural auto encoder designs.
When describing each design, we argue that the ideal architecture should ensure that: (1) both encoder and decoder have recurrent connections; (2) there is feedback from decoder to encoder; (3) capable of utilizing long-term temporal correlation. FRAE satisfies all 3.
