1,488 research outputs found
Spoken content retrieval: A survey of techniques and technologies
Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR
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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Modularity and Neural Integration in Large-Vocabulary Continuous Speech Recognition
This Thesis tackles the problems of modularity in Large-Vocabulary Continuous Speech Recognition with use of Neural Network
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