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The decade from 2010 to 2020 saw remarkable improvements in automatic speech recognition. Many people now use speech recognition on a daily basis, for example to perform voice search queries, send text messages, and interact with voice assistants like Amazon Alexa and Siri by Apple. Before 2010 most people rarely used speech recognition. Given the remarkable changes in the state of speech recognition over the previous decade, what can we expect over the coming decade? I attempt to forecast the state of speech recognition research and applications by the year 2030. While the changes to general speech recognition accuracy will not be as dramatic as in the previous decade, I suggest we have an exciting decade of progress in speech technology ahead of us.
In our previous work we demonstrated that a single headed attention encoder-decoder model is able to reach state-of-the-art results in conversational speech recognition. In this paper, we further improve the results for both Switchboard 300 and 2000.
Recent success of the Tacotron speech synthesis architecture and its variants in producing natural sounding multi-speaker synthesized speech has raised the exciting possibility of replacing expensive, manually transcribed, domain-specific, human spee
Automatic Speech Recognition (ASR) using multiple microphone arrays has achieved great success in the far-field robustness. Taking advantage of all the information that each array shares and contributes is crucial in this task. Motivated by the advan
We present Espresso, an open-source, modular, extensible end-to-end neural automatic speech recognition (ASR) toolkit based on the deep learning library PyTorch and the popular neural machine translation toolkit fairseq. Espresso supports distributed
This paper proposes serialized output training (SOT), a novel framework for multi-speaker overlapped speech recognition based on an attention-based encoder-decoder approach. Instead of having multiple output layers as with the permutation invariant t