48,239 research outputs found
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
DNN adaptation by automatic quality estimation of ASR hypotheses
In this paper we propose to exploit the automatic Quality Estimation (QE) of
ASR hypotheses to perform the unsupervised adaptation of a deep neural network
modeling acoustic probabilities. Our hypothesis is that significant
improvements can be achieved by: i)automatically transcribing the evaluation
data we are currently trying to recognise, and ii) selecting from it a subset
of "good quality" instances based on the word error rate (WER) scores predicted
by a QE component. To validate this hypothesis, we run several experiments on
the evaluation data sets released for the CHiME-3 challenge. First, we operate
in oracle conditions in which manual transcriptions of the evaluation data are
available, thus allowing us to compute the "true" sentence WER. In this
scenario, we perform the adaptation with variable amounts of data, which are
characterised by different levels of quality. Then, we move to realistic
conditions in which the manual transcriptions of the evaluation data are not
available. In this case, the adaptation is performed on data selected according
to the WER scores "predicted" by a QE component. Our results indicate that: i)
QE predictions allow us to closely approximate the adaptation results obtained
in oracle conditions, and ii) the overall ASR performance based on the proposed
QE-driven adaptation method is significantly better than the strong, most
recent, CHiME-3 baseline.Comment: Computer Speech & Language December 201
How conscious experience and working memory interact
Active components of classical working memory are conscious, but traditional theory does not account for this fact. Global Workspace theory suggests that consciousness is needed to recruit unconscious specialized networks that carry out detailed working memory functions. The IDA model provides a fine-grained analysis of this process, specifically of two classical workingmemory tasks, verbal rehearsal and the utilization of a visual image. In the process, new light is shed on the interactions between conscious and unconscious\ud
aspects of working memory
Interact: A Mixed Reality Virtual Survivor for Holocaust Testimonies
In this paper we present Interact---a mixed reality virtual survivor for Holocaust education. It was created to preserve the powerful and engaging experience of listening to, and interacting with, Holocaust survivors, allowing future generations of audience access to their unique stories. Interact demonstrates how advanced filming techniques, 3D graphics and natural language processing can be integrated and applied to specially-recorded testimonies to enable users to ask questions and receive answers from that virtualised individuals. This provides a new and rich interactive narratives of remembrance to engage with primary testimony. We discuss the design and development of Interact, and argue that this new form of mixed reality is promising media to overcome the uncanny valley
Human and Machine Speaker Recognition Based on Short Trivial Events
Trivial events are ubiquitous in human to human conversations, e.g., cough,
laugh and sniff. Compared to regular speech, these trivial events are usually
short and unclear, thus generally regarded as not speaker discriminative and so
are largely ignored by present speaker recognition research. However, these
trivial events are highly valuable in some particular circumstances such as
forensic examination, as they are less subjected to intentional change, so can
be used to discover the genuine speaker from disguised speech. In this paper,
we collect a trivial event speech database that involves 75 speakers and 6
types of events, and report preliminary speaker recognition results on this
database, by both human listeners and machines. Particularly, the deep feature
learning technique recently proposed by our group is utilized to analyze and
recognize the trivial events, which leads to acceptable equal error rates
(EERs) despite the extremely short durations (0.2-0.5 seconds) of these events.
Comparing different types of events, 'hmm' seems more speaker discriminative.Comment: ICASSP 201
Detecting gross alignment errors in the Spoken British National Corpus
The paper presents methods for evaluating the accuracy of alignments between
transcriptions and audio recordings. The methods have been applied to the
Spoken British National Corpus, which is an extensive and varied corpus of
natural unscripted speech. Early results show good agreement with human ratings
of alignment accuracy. The methods also provide an indication of the location
of likely alignment problems; this should allow efficient manual examination of
large corpora. Automatic checking of such alignments is crucial when analysing
any very large corpus, since even the best current speech alignment systems
will occasionally make serious errors. The methods described here use a hybrid
approach based on statistics of the speech signal itself, statistics of the
labels being evaluated, and statistics linking the two.Comment: Four pages, 3 figures. Presented at "New Tools and Methods for
Very-Large-Scale Phonetics Research", University of Pennsylvania, January
28-31, 201
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