[PDF] Top 20 Model adaptation and adaptive training for the recognition of dysarthric speech
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Model adaptation and adaptive training for the recognition of dysarthric speech
... to model dysarthric speech in general, SAT and SI-03 sys- tems were not significantly ...of dysarthric speech at an intra and in- ter speaker ...in dysarthric speech can ... See full document
7
Pronunciation Adaptation For Disordered Speech Recognition Using State Specific Vectors of Phone Cluster Adaptive Training
... of dysarthric speech recognition systems by handling pronunciation ...each dysarthric speaker using SSV from Phone-CAT model is dis- ...Phone-CAT model handles the data ... See full document
7
The Use of Adaptive Frame for Speech Recognition
... successful speech recognition systems mainly use Hidden Markov Model (HMM) for acoustic mod- ...continuous speech recog- nition field ...of speech recognition, a great deal of ... See full document
7
Combining feature and model-based adaptation of RNNLMs for multi-genre broadcast speech recognition
... for training the baseline 4-gram LM. For acoustic modelling, 700h. of speech was selected from the training set based on word matching error rate (WMER) and confidence scores ...for training ... See full document
6
Dysarthric Speech Recognition and Offline Handwriting Recognition using Deep Neural Networks
... speaker adaptation technique like Maximum Likeli- hood Linear regression (MLLR) transformation followed by MAP adaptation using GMM-HMM, along with speaker specific dictionaries based on acous- tic ... See full document
101
Phone Labeling Based on the Probabilistic Representation for Dysarthric Speech Recognition
... discuss speech recognition for persons with articulation disorders resulting from athetoid cerebral ...the speech style for a person with this type of articulation disorder is quite different from a ... See full document
5
Recurrent neural network language model adaptation for multi-genre broadcast speech recognition and alignment
... 3) Model-based Adaptation Results: The experiments show that LHN adaptation layer fine-tuning of RNNLMs, outper- forms full model fine-tuning in terms of PPL, for both LM 2 and LM1&LM 2 ... See full document
12
Histogram Equalization to Model Adaptation for Robust Speech Recognition
... both training and test data after estimating the reference CDFs from all training ...based model adaptation, the HEQ and proposed variance adaptation techniques are applied to the ... See full document
8
Chameleon: A Language Model Adaptation Toolkit for Automatic Speech Recognition of Conversational Speech
... graphical model of CSTM in Figure 2(d), we can see that CSTM represents the words in a speech dialogue corpus D as mixtures of K “top- ics” and each “topic” is represented as a multino- mial distribution ... See full document
6
A Robust Observation Model for Automatic Speech Recognition with Adaptive Thresholding
... By adaptive thresholding speech and non-speech discrimination is done after reading an audio ....Observation Model used to find out a relation between clean speech signal and noisy ... See full document
6
Features and Model Adaptation Techniques for Robust Speech Recognition: A Review
... The speech recognition is a pattern recognition task and Artificial Neural Network (ANN) is a good ...and speech signal is dynamic as it varies over time as it ...in speech signals ... See full document
14
Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers
... (Speech Training And Recog- nition for Dysarthric Users of Speech Technology) [16, 27–29] has developed speech technology for people with severe ...of training data and the ... See full document
14
State Transition Interpolation and MAP Adaptation for HMM based Dysarthric Speech Recognition
... MAP adaptation involves the use of prior knowledge about the model parameter ...the model are likely to be (before observing any adaptation data) using the prior knowledge, we might well be ... See full document
8
Dysarthric speech evaluation: automatic and perceptual approaches
... of speech segments into normal and abnormal phones ...the speech utterances into phones is carried out thanks to an automatic text- constrained phone alignment tool (Laaridh et ...the speech signal ... See full document
7
EMNLP versus ACL: Analyzing NLP research over time
... Parsing Model, Dependency Trees 12 Words, Other Hand, Natural Language, Other Words, Corpus, Language Processing, Model, Information, TestSet, Language 13 Pos Tagging, Pos Tags, Word Segmentation, Pos Tag, ... See full document
5
A Study on Speaker Adaptive Speech Recognition
... A Study on Speaker Adaptive Speech Recognition A Study on Speaker Adaptive Speech Recognition X D Huang School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 A B S T R A C T Speak[.] ... See full document
6
Robust Feature Extraction to Utterance Fluctuation of Articulation Disorders Based on Random Projection
... of speech recognition. Therefore, we recorded speech data for a person with an artic- ulation disorder who uttered each of the words five times, and investigated the influence of the unstable ... See full document
5
Dual supervised learning for non-native speech recognition
... two speech- related domains: speech samples without corresponding transcripts and text corpora without corresponding speech samples, to train speech recognition classifiers in a way ... See full document
10
Acoustic transformations to improve the intelligibility of dysarthric speech
... Although dysarthric utterances are likely to be contextualized within meaningful conversations in real-world situ- ations, such pragmatic aspects of discourse are not considered here in order to concentrate on ... See full document
11
Review on Speech Assistive Technologies for Dysarthric Patients
... language model are used to recognise the speech ...A model based on a discriminative approach finds the conditional distribution using a parametric model, where the parameters are determined ... See full document
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