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Phil Woodland - Professor of Information Engineering
mi.eng.cam.ac.uk/~pcw/19 Apr 2016: Current & New Research Projects. -
Segment Modelling Interest Group
mi.eng.cam.ac.uk/~ar527/smig.html24 Jun 2016: This colloboration has two primary aims:. conduct new research in the area. -
Knill_IS2014_slides.dvi
mi.eng.cam.ac.uk/~kmk/presentations/Interspeech2014_Sep14_Knill.pdf12 May 2016: Cambridge University Engineering Department. Interspeech 2014. IARPA Babel Program. • Goal - rapidly develop spoken term detection in new languages. – ... Reduce overhead in deploying new language? • Zero acoustic resources. – -
Stimulated Deep Neural Network for Speech Recognition
mi.eng.cam.ac.uk/~mjfg/interspeech16_stimu.pdf26 Sep 2016: English broadcast news(BN) transcription task and a Javanese conversational telephonespeech (CTS) task from the IARPA Babel program. ... English broadcast news(BN) task and a Javanese conversational telephone speech taskfrom the IARPA Babel program. -
Investigation of multilingual speech-to-text systems for use in…
mi.eng.cam.ac.uk/~kmk/presentations/UEdin_Feb14_Knill.pdf12 May 2016: Multilingual STT for Spoken Term Detection. IARPA Babel Program. • Goal - rapidly develop spoken term detection in new languages. – ... Reduce overhead in deploying new language? • Language Independent Acoustic Models. – -
Combining I-vector Representation and Structured Neural Networks for…
mi.eng.cam.ac.uk/~mjfg/icassp16_wu.pdf5 Apr 2016: English Broadcast News (BN) transcription task.Two distinct sets of test data are examined. ... The proposed approachesare evaluated on the utterance-level unsupervised adaptation of alarge vocabulary continuous English broadcast news transcriptiontask. -
Knill_CUEDSeminar_20140403.dvi
mi.eng.cam.ac.uk/~kmk/presentations/CUED_Apr14_Knill.pdf12 May 2016: Multilingual STT for Spoken Term Detection. IARPA Babel Program. • Goal - rapidly develop spoken term detection in new languages. – ... Reduce overhead in deploying new language? • Language Independent Acoustic Models. – -
Investigation of back-off based interpolation between Recurrent…
mi.eng.cam.ac.uk/~mjfg/asru15-chen.pdf11 Mar 2016: layer. The hidden layer compresses the information from these twoinputs and computes a new representation vi1 using a sigmoid ac-tivation to achieve non-linearity. -
Deep Learning for Speech Processing - An NST Perspective
mi.eng.cam.ac.uk/~mjfg/NST_2016.pdf29 Sep 2016: 5. ConclusionsIn this paper, we proposed a new training criterion for DNN-based speech synthesis, which minimises the trajectory errorrather than frame-by-frame error. -
slides_part2.dvi
mi.eng.cam.ac.uk/~kmk/presentations/TutorialIC_Sep2015_part2_Knill.pdf12 May 2016: 1. Add new hidden layer with random initial values2. Train the new network - usually restrict number of iterations3. -
MULTILINGUAL REPRESENTATIONS FOR LOW RESOURCE SPEECH RECOGNITION AND…
mi.eng.cam.ac.uk/~mjfg/asru15_cui.pdf23 May 2016: In each fold, a newalignment is generated with the current model and a new decisiontree is built on top the alignment. -
slides_part1.dvi
mi.eng.cam.ac.uk/~kmk/presentations/TutorialIC_Sep2015_part1_Knill.pdf12 May 2016: EM an iterative scheme involving two stages:. – Expectation: accumulate statistics given current model parameters– Maximisation: estimate new model parameters. • -
4F10: Deep Learning
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect6.pdf8 Nov 2016: at iteration τ is g[τ]• after new update θ[τ] the gradient should be zero. • -
Structured and InûniteDiscriminative Models for Speech Recognition…
mi.eng.cam.ac.uk/~mjfg/thesis_jy308.pdf26 Jul 2016: Structured and InûniteDiscriminative Models for. Speech Recognition. Jingzhou YangHomerton College. Department of Engineering. University of Cambridge. A thesis submitted to the University of Cambridge for the degree of. Doctor of Philosophy.
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