Search

5 Apr 2016: Speech andSignal Processing, ICASSP 2015, South Brisbane, Queens-land, Australia, April 19-24, 2015, 2015, pp. ... IEEE, 2015, pp. 4315–4319. [24] Mark JF Gales, “Cluster adaptive training of hidden markovmodels,” Speech and Audio Processing, IEEE

24 Jun 2016: Such lattices can be efficiently generatedusing standard HMM-based approaches [24]. It is simple to no-tice that the inference problem in equation (5) or its lattice basedapproximation includes equation (4) ... 24] J. J. Odell, “The use of context in

12 May 2016: CUED Lorelei TeamBABEL Program. Seminar at Edinburgh University February 2014 24.

10 Nov 2016: Data-based schemes instead. make use of data to initialise [27], train [20, 23] or adapt [24] the. ... The amount. of training data in VLLP conditions is 31,959 and 24,703 words.

12 Jul 2016: When the segment level features are used, the log-linear modelparameters η̂ could be considered as phone dependent acousticmodel scales [24]. ... In joint decoding, 2% relative WER performance gain wasachieved over the hybrid system, from 11.24% to

12 May 2016: CUED Lorelei Team. Babel ProgramSeminar at Cambridge University April 2014 24.

5 Apr 2016: When the segment level features are used, the log-linear modelparameters η̂ could be considered as phone dependent acousticmodel scales [24]. ... In joint decoding, 2% relative WER performance gain wasachieved over the hybrid system, from 11.24% to

12 May 2016: unintelligible, mispronounce, fragment words. • Convert PCM, 48KHz, 24-bit to A-law, 8KHz, 8-bit. ... yt. xt1. W. Timedelay. h. ht2. t1. • Use the hidden state values as a compact history representation [23, 24]. –

26 Sep 2016: Data-based schemes instead. make use of data to initialise [27], train [20, 23] or adapt [24] the. ... The amount. of training data in VLLP conditions is 31,959 and 24,703 words.

12 May 2016: Maximum Mutual Information (MMI) [23, 24]: maximise. Fmmi(λ) =1. R. ... Theory, 1991. Cambridge University. Engineering Department60. DNNs for Speech Processing. [24] P.

11 Mar 2016: In order to solve this problem, recentlythere has increasing research interest in deriving efficient paralleltraining algorithms for RNNLMs [22, 23, 24, 25]. ... 9.1 2.9 117.85GRNN 5472.1 170.0 24.3 7.9 2.4 117.6.

26 Sep 2016: This serves the basis ofstructured discriminative models including SSVMs. Classifica-tion is performed by solving a semi-Markov inference problem[24]:. ... 994–1006, 2010. [24] S. Sarawagi and W. W. Cohen, “Semi-Markov conditional randomfields for

12 Jul 2016: MPE— 7.15 11.06 14.37 24.54 16.79CML 6.95 11.00 14.29 24.39 16.68large-margin 7.02 10.92 14.16 24.28 ... Therefore the systems use graphemic lex-ica generated using an approach which is applicable to all Unicodecharacters [24].

11 Mar 2016: MPE— 7.15 11.06 14.37 24.54 16.79CML 6.95 11.00 14.29 24.39 16.68large-margin 7.02 10.92 14.16 24.28 ... Therefore the systems use graphemic lex-ica generated using an approach which is applicable to all Unicodecharacters [24].

29 Sep 2016: 24 of 67. S2S: Generative Models [5, 6]. • Consider two sequences L T: input: x1:T = {x1, x2,. , ... 47 of 67. ASR: Sequence Training [24]. • Cross-Entropy using fixed alignment standard criterion (RNN).

23 May 2016: The input features are 24-dimensionallog Mel magnitude spectrum filter banks, pitch, probability of voic-ing, and their derivatives. ... 24, no. 3, pp. 433–444, 2010. [36] Nobuyasu Itoh, Tara N Sainath, Dan Ning Jiang, Jie Zhou, andBhuvana Ramabhadran,

8 Nov 2016: represents element-wise multiplication between vectors. 24/68. Long-Short Term Memory Networks (reference) [13, 10]. ... Ẽ (θ[τ]) = E (θ[τ]) νw[τ]. 50/68. Dropout [24]. Input. xd.

26 Sep 2016: IEEE, 2011, pp.24–29. [9] R. Gemello, F. Mana, S. Scanzio, P.

26 Jul 2016: 24. 2.6 he framework of linear transform based adaptive training. 26. ... criterion (2.23), and it is deûned as follows:. [f(x). ]. =. {0 when f(x) < 0f(x) when f(x) 0 (2.24). Because of the max{} function, the objective function