1,106 research outputs found
Stochastic Pronunciation Modelling for Out-of-Vocabulary Spoken Term Detection
Spoken term detection (STD) is the name given to the task of searching large amounts of audio for occurrences of spoken terms, which are typically single words or short phrases. One reason that STD is a hard task is that search terms tend to contain a disproportionate number of out-of-vocabulary (OOV) words. The most common approach to STD uses subword units. This, in conjunction with some method for predicting pronunciations of OOVs from their written form, enables the detection of OOV terms but performance is considerably worse than for in-vocabulary terms. This performance differential can be largely attributed to the special properties of OOVs. One such property is the high degree of uncertainty in the pronunciation of OOVs. We present a stochastic pronunciation model (SPM) which explicitly deals with this uncertainty. The key insight is to search for all possible pronunciations when detecting an OOV term, explicitly capturing the uncertainty in pronunciation. This requires a probabilistic model of pronunciation, able to estimate a distribution over all possible pronunciations. We use a joint-multigram model (JMM) for this and compare the JMM-based SPM with the conventional soft match approach. Experiments using speech from the meetings domain demonstrate that the SPM performs better than soft match in most operating regions, especially at low false alarm probabilities. Furthermore, SPM and soft match are found to be complementary: their combination provides further performance gains
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
Alcohol Language Corpus
The Alcohol Language Corpus (ALC) is the first publicly available speech corpus comprising intoxicated and sober speech of 162 female and male German speakers.
Recordings are done in the automotive environment to allow for the development of automatic alcohol detection and to ensure a consistent acoustic environment for the alcoholized and the sober recording. The recorded speech covers a variety of contents and speech styles. Breath and blood alcohol concentration measurements are provided for all speakers. A transcription according to SpeechDat/Verbmobil standards and disfluency tagging as well as an automatic phonetic segmentation are part of the corpus. An Emu version of ALC allows easy access to basic speech parameters as well as the us of R for statistical analysis of selected parts of ALC. ALC is available without restriction for scientific or commercial use at the Bavarian Archive for Speech Signals
Evolutionary discriminative confidence estimation for spoken term detection
The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-011-0913-zSpoken term detection (STD) is the task of searching for occurrences
of spoken terms in audio archives. It relies on robust confidence estimation
to make a hit/false alarm (FA) decision. In order to optimize the decision
in terms of the STD evaluation metric, the confidence has to be discriminative.
Multi-layer perceptrons (MLPs) and support vector machines (SVMs) exhibit
good performance in producing discriminative confidence; however they are
severely limited by the continuous objective functions, and are therefore less
capable of dealing with complex decision tasks. This leads to a substantial
performance reduction when measuring detection of out-of-vocabulary (OOV)
terms, where the high diversity in term properties usually leads to a complicated
decision boundary.
In this paper we present a new discriminative confidence estimation approach
based on evolutionary discriminant analysis (EDA). Unlike MLPs and
SVMs, EDA uses the classification error as its objective function, resulting
in a model optimized towards the evaluation metric. In addition, EDA combines
heterogeneous projection functions and classification strategies in decision
making, leading to a highly flexible classifier that is capable of dealing
with complex decision tasks. Finally, the evolutionary strategy of EDA reduces the risk of local minima. We tested the EDA-based confidence with a
state-of-the-art phoneme-based STD system on an English meeting domain
corpus, which employs a phoneme speech recognition system to produce lattices
within which the phoneme sequences corresponding to the enquiry terms
are searched. The test corpora comprise 11 hours of speech data recorded with
individual head-mounted microphones from 30 meetings carried out at several
institutes including ICSI; NIST; ISL; LDC; the Virginia Polytechnic Institute
and State University; and the University of Edinburgh. The experimental results
demonstrate that EDA considerably outperforms MLPs and SVMs on
both classification and confidence measurement in STD, and the advantage
is found to be more significant on OOV terms than on in-vocabulary (INV)
terms. In terms of classification performance, EDA achieved an equal error
rate (EER) of 11% on OOV terms, compared to 34% and 31% with MLPs and
SVMs respectively; for INV terms, an EER of 15% was obtained with EDA
compared to 17% obtained with MLPs and SVMs. In terms of STD performance
for OOV terms, EDA presented a significant relative improvement of
1.4% and 2.5% in terms of average term-weighted value (ATWV) over MLPs
and SVMs respectively.This work was partially supported by the French Ministry of Industry
(Innovative Web call) under contract 09.2.93.0966, ‘Collaborative Annotation for Video
Accessibility’ (ACAV) and by ‘The Adaptable Ambient Living Assistant’ (ALIAS) project
funded through the joint national Ambient Assisted Living (AAL) programme
Augmented set of features for confidence estimation in spoken term detection
Discriminative confidence estimation along with confidence normalisation have been shown to construct robust decision maker modules in spoken term detection (STD) systems. Discriminative confidence estimation, making use of termdependent features, has been shown to improve the widely used lattice-based confidence estimation in STD. In this work, we augment the set of these term-dependent features and show a significant improvement in the STD performance both in terms of ATWV and DET curves in experiments conducted on a Spanish geographical corpus. This work also proposes a multiple linear regression analysis to carry out the feature selection. Next, the most informative features derived from it are used within the discriminative confidence on the STD system
Improving Searchability of Automatically Transcribed Lectures Through Dynamic Language Modelling
Recording university lectures through lecture capture systems is increasingly common.
However, a single continuous audio recording is often unhelpful for users, who may wish
to navigate quickly to a particular part of a lecture, or locate a specific lecture within a set
of recordings.
A transcript of the recording can enable faster navigation and searching. Automatic speech
recognition (ASR) technologies may be used to create automated transcripts, to avoid the
significant time and cost involved in manual transcription.
Low accuracy of ASR-generated transcripts may however limit their usefulness. In
particular, ASR systems optimized for general speech recognition may not recognize the
many technical or discipline-specific words occurring in university lectures. To improve
the usefulness of ASR transcripts for the purposes of information retrieval (search) and
navigating within recordings, the lexicon and language model used by the ASR engine may
be dynamically adapted for the topic of each lecture.
A prototype is presented which uses the English Wikipedia as a semantically dense, large
language corpus to generate a custom lexicon and language model for each lecture from a
small set of keywords. Two strategies for extracting a topic-specific subset of Wikipedia
articles are investigated: a naïve crawler which follows all article links from a set of seed
articles produced by a Wikipedia search from the initial keywords, and a refinement which
follows only links to articles sufficiently similar to the parent article. Pair-wise article
similarity is computed from a pre-computed vector space model of Wikipedia article term
scores generated using latent semantic indexing.
The CMU Sphinx4 ASR engine is used to generate transcripts from thirteen recorded
lectures from Open Yale Courses, using the English HUB4 language model as a reference
and the two topic-specific language models generated for each lecture from Wikipedia
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