Speaker adaptation using Deep Neural Networks
An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation
... a speaker-independent acoustic transformation and use speaker- dependent parameters to model speaker ...in speaker adaptation, we hypothesize that language adaptive training (LAT) could ...
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Using Deep Neural Networks for Speaker Diarisation
... a speaker talking after other participants have been speaking using a ...DHC using the E-HMM approach, nevertheless it is similar to the rest in the fact that it applies the HMM-GMM topology for both ...
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Improved speaker independent lip reading using speaker adaptive training and deep neural networks
... Recently, Deep Neural Networks (DNN) with different deep learning architectures have proved to be successful in Automatic Speech Recognition (ASR) and other areas of ma- chine learning ...
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ASVtorch Toolkit: Speaker Verification with Deep Neural Networks
... automatic speaker verification (ASV) — recognizing a person from his/her ...particularly deep learning meth- ...Python using the widely used PyTorch machine learning ...
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Bi Transferring Deep Neural Networks for Domain Adaptation
... domain adaptation methods, SCL, MCT, SFA, PJNMF, SDA, mSDA and TLDA, consistently outperform the baseline method across all the 12 tasks, which demonstrates that the transferred knowledge from the source domain to ...
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I-Vector Estimation Using Informative Priors for Adaptation of Deep Neural Networks
... In the second block of the table, the utterance level test i-vectors improve further the WER (“+iv-utter”) for both ver- sions of dev03 set, maybe because of less confusions related to noise variations. The remaining ...
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Expressive visual text to speech and expression adaptation using deep neural networks
... to networks with output lay- ers that were estimated using regularised least ...the adaptation data and for α > 1000, the layer weights are too small and do not model the adaptation data ...
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Deep Neural Networks for Channel Compensated i-Vectors in Speaker Recognition
... layer using RBMs as explained above using all the background vectors as feeding ...general, neural network parameters are initialized randomly but it has been shown [21] that the pre- trained ...
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Domain Adaptation through Deep Neural Networks for Health Informatics
... subsets, using the holdout method and preserving the integrity of the time series, in order to have a permanent training set for the optimization of the weights and biases of the neural network, a ...
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Using Graph Neural Networks to model the performance of Deep Neural Networks
... Graph Neural Network-based performance model to estimate the run times of deep learning pipelines implemented using the Halide ...from deep-learning programs implemented in Halide as input and ...
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Speaker identification and clustering using convolutional neural networks
... the speaker recognition pipeline. We apply Convolutional Neural Networks (CNNs) [12] on spectrograms in order to be able to learn speaker-specific features from a rich acoustic source ...
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Iterative deep neural networks for speaker-independent binaural blind speech separation
... that using the proposed feature, either “Convert-Hybrid” or “Convert- MLP” gained the best and consistent performance over dif- ferent gender combinations, especially for “Convert-Hybrid”, where the worst ...
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Multi-task deep neural network acoustic models with model adaptation using discriminative speaker identity for whisper recognition
... each speaker in the test and enrollment sets are used to extract an i-vector (denoted ‘max’), an ...that speaker identity information is de- graded in whispers, so fewer utterances may be insufficient to ...
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Text Processing Using Deep Neural Networks
... 1 Úvod Tato práce za ne postupn popisovat architekturu neuronov˝ch sítí, dále bude obsahovat popis technologií, které budou pozd ji pouûity v r zn˝ch fázích m˝ch experiment . Dále zde budou uvedeny základní informace o ...
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Lip Reading using Deep Neural Networks
... Chapter 1 Introduction 1.1 Motivation The problem of lip reading is a very present topic that has not yet been fully resolved and is a great challenge for solving using artificial intelligence and machine ...
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Image Description using Deep Neural Networks
... Convolutional Neural Networks Convolutional Neural Networks (CNNs) are a specific form of FNNs that explicitly assume the inputs to the network be structured samples, such as audio signals or ...
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Headline Generation using Deep Neural Networks
... model using a ...summarization using the elements of the language such as semantics and ...sentence using BBN ...by using linguistically motivated ...
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ECG Biometrics using Deep Neural Networks
... that Deep Learning can be applied successfully in the analy- sis of physiological signals for biometric ...Convolutional Neural Networks and Recurrent Neural Networks, which may receive ...
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Deep Belief Networks Using Convolution Neural Networks Algorithm
... of deep learning is not new to higher educatio n. However, deep learning has drawn more attention in recent years as institutions attempt to tap their student’s full learning ...of deep learning, ...
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Speech dereverberation for enhancement and recognition using dynamic features constrained deep neural networks and feature adaptation
... 6.3.4 Effect of dynamic feature estimation We now investigate the effect of predicting dynamic log-magnitude spectra and using them to improve the static spectra. The results are shown in the rows DNN7, DNN7LS, ...
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