117 research outputs found
Streaming End-to-end Speech Recognition For Mobile Devices
End-to-end (E2E) models, which directly predict output character sequences
given input speech, are good candidates for on-device speech recognition. E2E
models, however, present numerous challenges: In order to be truly useful, such
models must decode speech utterances in a streaming fashion, in real time; they
must be robust to the long tail of use cases; they must be able to leverage
user-specific context (e.g., contact lists); and above all, they must be
extremely accurate. In this work, we describe our efforts at building an E2E
speech recognizer using a recurrent neural network transducer. In experimental
evaluations, we find that the proposed approach can outperform a conventional
CTC-based model in terms of both latency and accuracy in a number of evaluation
categories
Adapting End-to-End Speech Recognition for Readable Subtitles
Automatic speech recognition (ASR) systems are primarily evaluated on
transcription accuracy. However, in some use cases such as subtitling, verbatim
transcription would reduce output readability given limited screen size and
reading time. Therefore, this work focuses on ASR with output compression, a
task challenging for supervised approaches due to the scarcity of training
data. We first investigate a cascaded system, where an unsupervised compression
model is used to post-edit the transcribed speech. We then compare several
methods of end-to-end speech recognition under output length constraints. The
experiments show that with limited data far less than needed for training a
model from scratch, we can adapt a Transformer-based ASR model to incorporate
both transcription and compression capabilities. Furthermore, the best
performance in terms of WER and ROUGE scores is achieved by explicitly modeling
the length constraints within the end-to-end ASR system.Comment: IWSLT 202
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
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