114 research outputs found
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
We present an MatchboxNet - an end-to-end neural network for speech command
recognition. MatchboxNet is a deep residual network composed from blocks of 1D
time-channel separable convolution, batch-normalization, ReLU and dropout
layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech
Commands dataset while having significantly fewer parameters than similar
models. The small footprint of MatchboxNet makes it an attractive candidate for
devices with limited computational resources. The model is highly scalable, so
model accuracy can be improved with modest additional memory and compute.
Finally, we show how intensive data augmentation using an auxiliary noise
dataset improves robustness in the presence of background noise
SpellMapper: A non-autoregressive neural spellchecker for ASR customization with candidate retrieval based on n-gram mappings
Contextual spelling correction models are an alternative to shallow fusion to
improve automatic speech recognition (ASR) quality given user vocabulary. To
deal with large user vocabularies, most of these models include candidate
retrieval mechanisms, usually based on minimum edit distance between fragments
of ASR hypothesis and user phrases. However, the edit-distance approach is
slow, non-trainable, and may have low recall as it relies only on common
letters. We propose: 1) a novel algorithm for candidate retrieval, based on
misspelled n-gram mappings, which gives up to 90% recall with just the top 10
candidates on Spoken Wikipedia; 2) a non-autoregressive neural model based on
BERT architecture, where the initial transcript and ten candidates are combined
into one input. The experiments on Spoken Wikipedia show 21.4% word error rate
improvement compared to a baseline ASR system.Comment: Accepted by INTERSPEECH 202
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