2 research outputs found
English Broadcast News Speech Recognition by Humans and Machines
With recent advances in deep learning, considerable attention has been given
to achieving automatic speech recognition performance close to human
performance on tasks like conversational telephone speech (CTS) recognition. In
this paper we evaluate the usefulness of these proposed techniques on broadcast
news (BN), a similar challenging task. We also perform a set of recognition
measurements to understand how close the achieved automatic speech recognition
results are to human performance on this task. On two publicly available BN
test sets, DEV04F and RT04, our speech recognition system using LSTM and
residual network based acoustic models with a combination of n-gram and neural
network language models performs at 6.5% and 5.9% word error rate. By achieving
new performance milestones on these test sets, our experiments show that
techniques developed on other related tasks, like CTS, can be transferred to
achieve similar performance. In contrast, the best measured human recognition
performance on these test sets is much lower, at 3.6% and 2.8% respectively,
indicating that there is still room for new techniques and improvements in this
space, to reach human performance levels.Comment: \copyright 2019 IEEE. Personal use of this material is permitted.
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Speakerly: A Voice-based Writing Assistant for Text Composition
We present Speakerly, a new real-time voice-based writing assistance system
that helps users with text composition across various use cases such as emails,
instant messages, and notes. The user can interact with the system through
instructions or dictation, and the system generates a well-formatted and
coherent document. We describe the system architecture and detail how we
address the various challenges while building and deploying such a system at
scale. More specifically, our system uses a combination of small, task-specific
models as well as pre-trained language models for fast and effective text
composition while supporting a variety of input modes for better usability.Comment: Accepted at EMNLP 2023 Industry Trac