8,406 research outputs found
Comparing Human and Machine Errors in Conversational Speech Transcription
Recent work in automatic recognition of conversational telephone speech (CTS)
has achieved accuracy levels comparable to human transcribers, although there
is some debate how to precisely quantify human performance on this task, using
the NIST 2000 CTS evaluation set. This raises the question what systematic
differences, if any, may be found differentiating human from machine
transcription errors. In this paper we approach this question by comparing the
output of our most accurate CTS recognition system to that of a standard speech
transcription vendor pipeline. We find that the most frequent substitution,
deletion and insertion error types of both outputs show a high degree of
overlap. The only notable exception is that the automatic recognizer tends to
confuse filled pauses ("uh") and backchannel acknowledgments ("uhhuh"). Humans
tend not to make this error, presumably due to the distinctive and opposing
pragmatic functions attached to these words. Furthermore, we quantify the
correlation between human and machine errors at the speaker level, and
investigate the effect of speaker overlap between training and test data.
Finally, we report on an informal "Turing test" asking humans to discriminate
between automatic and human transcription error cases
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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Access to recorded interviews: A research agenda
Recorded interviews form a rich basis for scholarly inquiry. Examples include oral histories, community memory projects, and interviews conducted for broadcast media. Emerging technologies offer the potential to radically transform the way in which recorded interviews are made accessible, but this vision will demand substantial investments from a broad range of research communities. This article reviews the present state of practice for making recorded interviews available and the state-of-the-art for key component technologies. A large number of important research issues are identified, and from that set of issues, a coherent research agenda is proposed
The Microsoft 2017 Conversational Speech Recognition System
We describe the 2017 version of Microsoft's conversational speech recognition
system, in which we update our 2016 system with recent developments in
neural-network-based acoustic and language modeling to further advance the
state of the art on the Switchboard speech recognition task. The system adds a
CNN-BLSTM acoustic model to the set of model architectures we combined
previously, and includes character-based and dialog session aware LSTM language
models in rescoring. For system combination we adopt a two-stage approach,
whereby subsets of acoustic models are first combined at the senone/frame
level, followed by a word-level voting via confusion networks. We also added a
confusion network rescoring step after system combination. The resulting system
yields a 5.1\% word error rate on the 2000 Switchboard evaluation set
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