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Audio tagging aims to assign one or several tags to an audio clip. Most of the datasets are weakly labelled, which means only the tags of the clip are known, without knowing the occurrence time of the tags. The labeling of an audio clip is often based on the audio events in the clip and no event level label is provided to the user. Previous works have used the bag of frames model assume the tags occur...
Recently, the signal captured from a laser Doppler vibrometer (LDV) sensor been used to improve the noise robustness automatic speech recognition (ASR) systems by enhancing the acoustic signal prior to feature extraction. This study proposes another approach in which auxiliary features extracted from the LDV signal are used alongside conventional acoustic features to further improve ASR performance...
In this paper, a novel deep neural network (DNN) architecture is proposed to generate the speech features of both the target speaker and interferer for speech separation without using any prior information about the interfering speaker. DNN is adopted here to directly model the highly nonlinear relationship between speech features of the mixed signals and the two competing speakers. Experimental results...
This paper proposes a novel data-driven approach based on deep neural networks (DNNs) for single-channel speech separation. DNN is adopted to directly model the highly non-linear relationship of speech features between a target speaker and the mixed signals. Both supervised and semi-supervised scenarios are investigated. In the supervised mode, both identities of the target speaker and the interfering...
The search for out of vocabulary (OOV) query terms in spoken term detection (STD) task is addressed in this paper. The phone level fragment with word-position marker is naturally adopted as the speech recognition decoding unit. Then the triphone confusion matrix (TriCM) is used to expand the query space to compensate for speech recognition errors. And we also propose a new approach to construct triphone...
Spoken term detection (STD) is a task for open vocabulary search in large recordings of speech. Although the term detection performance for in-vocabulary (INV) terms has achieved a great improvement, the detection performance for out of vocabulary (OOV) terms is still disappointing. In this paper, we propose to combine fragment-based with syllable-based search into a hybrid STD system for OOV terms...
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