Detail výsledku
HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition
State-of-the-art ASR systems have achieved promising results
by modeling local and global interactions separately. While the
former can be computed efficiently, global interactions are usu-
ally modeled via attention mechanisms, which are expensive for
long input sequences. Here, we address this by extending Hy-
perMixer, an efficient alternative to attention exhibiting linear
complexity, to the Conformer architecture for speech recogni-
tion, leading to HyperConformer. In particular, multi-head Hy-
perConformer achieves comparable or higher recognition per-
formance while being more efficient than Conformer in terms of
inference speed, memory, parameter count, and available train-
ing data. HyperConformer achieves a word error rate of 2.9%
on LibriSpeech test-clean with less than 8M neural parameters
and a peak memory during training of 5.7GB, hence trainable
with accessible hardware. Encoder speed is between 38% on
mid-length speech and 56% on long speech faster than an equiv-
alent Conformer.1)
Hypernetworks, HyperMixer, Efficient Auto-
matic Speech Recognition, LibriSpeech, SpeechBrain
@inproceedings{BUT187786,
author="MAI, F. and ZULUAGA-GOMEZ, J. and PARCOLLET, T. and MOTLÍČEK, P.",
title="HyperConformer: Multi-head HyperMixer for Efficient Speech Recognition",
booktitle="Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH",
year="2023",
journal="Proceedings of Interspeech",
volume="2023",
number="08",
pages="2213--2217",
publisher="International Speech Communication Association",
address="Dublin",
doi="10.21437/Interspeech.2023-1611",
issn="1990-9772",
url="https://www.isca-archive.org/interspeech_2023/mai23_interspeech.pdf"
}