Robust speech recognition using consensus function based on multi-layer networks
The clustering ensembles mingle numerous partitions of a specified data into a single clustering solution. Clustering ensemble has emerged as a potent approach for ameliorating both the forcefulness and the stability of unsupervised classification results. One of the major problems in clustering ensembles is to find the best consensus function. Finding final partition from different clustering results requires skillfulness and robustness of the classification algorithm. In addition, the major problem with the consensus function is its sensitivity to the used data sets quality. This limitation is due to the existence of noisy, silence or redundant data. This paper proposes a novel consensus function of cluster ensembles based on Multilayer networks technique and a maintenance database method. This maintenance database approach is used in order to handle any given noisy speech and, thus, to guarantee the quality of databases. This can generates good results and efficient data partitions. To show its effectiveness, we support our strategy with empirical evaluation using distorted speech from Aurora speech databases.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringClustering EnsembleGeneral ClassificationRobust Speech Recognitionspeech-recognitionSpeech RecognitionSimilar Papers 제목 키워드 기반
Modeling speech emotion with label variance and analyzing performance across speakers and unseen acoustic conditions
Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertainty in labels due to grader opinion varia…
Emotion RecognitionOverall - TestWhat does a network layer hear? Analyzing hidden representations of end-to-end ASR through speech synthesis
End-to-end speech recognition systems have achieved competitive results compared to traditional systems. However, the complex transformations involved between layers given highly variable acoustic signals are hard to ana…
Speaker VerificationSpeech Enhancementspeech-recognitionSpeech Recognition+1Adaptive Activation Network For Low Resource Multilingual Speech Recognition
Low resource automatic speech recognition (ASR) is a useful but thorny task, since deep learning ASR models usually need huge amounts of training data. The existing models mostly established a bottleneck (BN) layer by pr…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)speech-recognitionSpeech RecognitionSequence Training and Adaptation of Highway Deep Neural Networks
Highway deep neural network (HDNN) is a type of depth-gated feedforward neural network, which has shown to be easier to train with more hidden layers and also generalise better compared to conventional plain deep neural …
speech-recognitionSpeech RecognitionHierarchical Multitask Learning for CTC-based Speech Recognition
Previous work has shown that neural encoder-decoder speech recognition can be improved with hierarchical multitask learning, where auxiliary tasks are added at intermediate layers of a deep encoder. We explore the effect…
Decoderspeech-recognitionSpeech Recognition