Darpa Timit:

Garofolo, John S.; Lamel, Lori; Fisher, William M.; Fiscus, Jonathan G.; Pallett, David S.; Dahlgren, Nancy L.

doi:10.6028/nist.ir.4930

Cited by 1,103 publications

(371 citation statements)

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“…To minimise any large noise and channel variations, the spectral content of the recordings was examined, and a number of early recordings, deemed to vary too greatly from the later recordings in terms of frequency content, were discarded. In addition to our ageing database, for background modelling two other data sources were used; the TIMIT corpus [10] and the 'University of Florida Vocal Aging Database 2 -Extemporaneous' (UFvadEX) [11].…”

Section: Speech Datamentioning

confidence: 99%

Effects of Long-Term Ageing on Speaker Verification

Kelly

Harte

2011

Lecture Notes in Computer Science

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Abstract. The changes that occur in the human voice due to ageing have been well documented. The impact of these changes on speaker verification is less clear. In this work, we examine the effect of long-term vocal ageing on a speaker verification system. On a cohort of 13 adult speakers, using a conventional GMM-UBM system, we carry out longitudinal testing of each speaker across a time span of 30-40 years. We uncover a progressive degradation in verification score as the time span between the training and test material increases. The addition of temporal information to the features causes the rate of degradation to increase. No significant difference was found between MFCC and PLP features. Subsequent experiments show that the effect of short-term ageing (<5 years) is not significant compared with normal inter-session variability. Above this time span however, ageing has a detrimental effect on verification. Finally, we show that the age of the speaker at the time of training influences the rate at which the verification scores degrade. Our results suggest that the verification score drop-off accelerates for speakers over the age of 60. The results presented are the first of their kind to quantify the effect of long-term vocal ageing on speaker verification.

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Section: Speech Datamentioning

confidence: 99%

Effects of Long-Term Ageing on Speaker Verification

Kelly

Harte

2011

Lecture Notes in Computer Science

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“…The speech samples chosen to illustrate the lateral inhibitory networks are from the TIMIT database [6]. We have chosen four samples with different utterances of varying lengths, spoken by two male speakers and two female speakers, see TABLE I.…”

Section: Pre-processing Of Speech Samplesmentioning

confidence: 99%

Recurrent lateral inhibitory spiking networks for speech enhancement

Wall

Glackin

Cannings

et al. 2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Abstract-Automatic speech recognition accuracy is affected adversely by the presence of noise. In this paper we present a novel noise removal and speech enhancement technique based on spiking neural network processing of speech data. The spiking network has a recurrent lateral topology that is biologically inspired, specifically by the inhibitory cells of the cochlear nucleus. The network can be configured for different acoustic environments and it will be demonstrated how the connectivity results in enhancement of temporal correlation between similar frequency bands and removal of uncorrelated noise sources. Demonstration of the speech enhancement capability will be provided with data taken from the TIMIT database with different levels of additive Gaussian white noise. Future directions for further development of this novel approach to noise removal and signal processing will also be discussed.

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“…An extreme example, Bard et al [6], compiled the DCIEM Map Task Corpus, which consists of taskoriented, unscripted dialogs in which one interlocutor describes a map route to the other, after both interlocutors had been subjected to 60 h of sleep deprivation and to one of three drug treatments. Another example is the TIMIT corpus [24], which contains broadband recordings of 630 speakers of eight major dialects of American English, each reading ten phonetically rich sentences.…”

Section: Definition Of a Corpusmentioning

confidence: 99%

Linguistic Annotation in/for Corpus Linguistics

Gries

Berez

2017

Handbook of Linguistic Annotation

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This article surveys linguistic annotation in corpora and corpus linguistics. We first define the concept of 'corpus' as a radial category and then, in Sect. 2, discuss a variety of kinds of information for which corpora are annotated and that are exploited in contemporary corpus linguistics. Section 3 then exemplifies many current formats of annotation with an eye to highlighting both the diversity of formats currently available and the emergence of XML annotation as, for now, the most widespread form of annotation. Section 4 summarizes and concludes with desiderata for future developments.

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Darpa Timit:

Cited by 1,103 publications

References 0 publications

Effects of Long-Term Ageing on Speaker Verification

Effects of Long-Term Ageing on Speaker Verification

Recurrent lateral inhibitory spiking networks for speech enhancement

Linguistic Annotation in/for Corpus Linguistics

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