7,941 research outputs found
Integrated speech and morphological processing in a connectionist continuous speech understanding for Korean
A new tightly coupled speech and natural language integration model is
presented for a TDNN-based continuous possibly large vocabulary speech
recognition system for Korean. Unlike popular n-best techniques developed for
integrating mainly HMM-based speech recognition and natural language processing
in a {\em word level}, which is obviously inadequate for morphologically
complex agglutinative languages, our model constructs a spoken language system
based on a {\em morpheme-level} speech and language integration. With this
integration scheme, the spoken Korean processing engine (SKOPE) is designed and
implemented using a TDNN-based diphone recognition module integrated with a
Viterbi-based lexical decoding and symbolic phonological/morphological
co-analysis. Our experiment results show that the speaker-dependent continuous
{\em eojeol} (Korean word) recognition and integrated morphological analysis
can be achieved with over 80.6% success rate directly from speech inputs for
the middle-level vocabularies.Comment: latex source with a4 style, 15 pages, to be published in computer
processing of oriental language journa
ORCA-SPOT: An Automatic Killer Whale Sound Detection Toolkit Using Deep Learning
Large bioacoustic archives of wild animals are an important source to identify reappearing communication patterns, which can then be related to recurring behavioral patterns to advance the current understanding of intra-specific communication of non-human animals. A main challenge remains that most large-scale bioacoustic archives contain only a small percentage of animal vocalizations and a large amount of environmental noise, which makes it extremely difficult to manually retrieve sufficient vocalizations for further analysis – particularly important for species with advanced social systems and complex vocalizations. In this study deep neural networks were trained on 11,509 killer whale (Orcinus orca) signals and 34,848 noise segments. The resulting toolkit ORCA-SPOT was tested on a large-scale bioacoustic repository – the Orchive – comprising roughly 19,000 hours of killer whale underwater recordings. An automated segmentation of the entire Orchive recordings (about 2.2 years) took approximately 8 days. It achieved a time-based precision or positive-predictive-value (PPV) of 93.2% and an area-under-the-curve (AUC) of 0.9523. This approach enables an automated annotation procedure of large bioacoustics databases to extract killer whale sounds, which are essential for subsequent identification of significant communication patterns. The code will be publicly available in October 2019 to support the application of deep learning to bioaoucstic research. ORCA-SPOT can be adapted to other animal species
Integrating Syntactic and Prosodic Information for the Efficient Detection of Empty Categories
We describe a number of experiments that demonstrate the usefulness of
prosodic information for a processing module which parses spoken utterances
with a feature-based grammar employing empty categories. We show that by
requiring certain prosodic properties from those positions in the input where
the presence of an empty category has to be hypothesized, a derivation can be
accomplished more efficiently. The approach has been implemented in the machine
translation project VERBMOBIL and results in a significant reduction of the
work-load for the parser.Comment: To appear in the Proceedings of Coling 1996, Copenhagen. 6 page
Transfer Learning for Speech and Language Processing
Transfer learning is a vital technique that generalizes models trained for
one setting or task to other settings or tasks. For example in speech
recognition, an acoustic model trained for one language can be used to
recognize speech in another language, with little or no re-training data.
Transfer learning is closely related to multi-task learning (cross-lingual vs.
multilingual), and is traditionally studied in the name of `model adaptation'.
Recent advance in deep learning shows that transfer learning becomes much
easier and more effective with high-level abstract features learned by deep
models, and the `transfer' can be conducted not only between data distributions
and data types, but also between model structures (e.g., shallow nets and deep
nets) or even model types (e.g., Bayesian models and neural models). This
review paper summarizes some recent prominent research towards this direction,
particularly for speech and language processing. We also report some results
from our group and highlight the potential of this very interesting research
field.Comment: 13 pages, APSIPA 201
Many uses, many annotations for large speech corpora: Switchboard and TDT as case studies
This paper discusses the challenges that arise when large speech corpora
receive an ever-broadening range of diverse and distinct annotations. Two case
studies of this process are presented: the Switchboard Corpus of telephone
conversations and the TDT2 corpus of broadcast news. Switchboard has undergone
two independent transcriptions and various types of additional annotation, all
carried out as separate projects that were dispersed both geographically and
chronologically. The TDT2 corpus has also received a variety of annotations,
but all directly created or managed by a core group. In both cases, issues
arise involving the propagation of repairs, consistency of references, and the
ability to integrate annotations having different formats and levels of detail.
We describe a general framework whereby these issues can be addressed
successfully.Comment: 7 pages, 2 figure
When Are Tree Structures Necessary for Deep Learning of Representations?
Recursive neural models, which use syntactic parse trees to recursively
generate representations bottom-up, are a popular architecture. But there have
not been rigorous evaluations showing for exactly which tasks this syntax-based
method is appropriate. In this paper we benchmark {\bf recursive} neural models
against sequential {\bf recurrent} neural models (simple recurrent and LSTM
models), enforcing apples-to-apples comparison as much as possible. We
investigate 4 tasks: (1) sentiment classification at the sentence level and
phrase level; (2) matching questions to answer-phrases; (3) discourse parsing;
(4) semantic relation extraction (e.g., {\em component-whole} between nouns).
Our goal is to understand better when, and why, recursive models can
outperform simpler models. We find that recursive models help mainly on tasks
(like semantic relation extraction) that require associating headwords across a
long distance, particularly on very long sequences. We then introduce a method
for allowing recurrent models to achieve similar performance: breaking long
sentences into clause-like units at punctuation and processing them separately
before combining. Our results thus help understand the limitations of both
classes of models, and suggest directions for improving recurrent models
Sperry Univac speech communications technology
Technology and systems for effective verbal communication with computers were developed. A continuous speech recognition system for verbal input, a word spotting system to locate key words in conversational speech, prosodic tools to aid speech analysis, and a prerecorded voice response system for speech output are described
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