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Visual words of Bag-of-Visual-Words (BoVW) framework are independent each other, which results in not only discarding spatial orders between visual words but also lacking semantic information. This study is inspired by word embeddings that a similar embedding procedure is applied to a large number of visual words. By this way, the corresponding embedding vectors of the visual words can be formulated...
sequence during training. This paper explores the design of an ASR-free end-to-end system for text query-based keyword search (KWS) from speech trained with minimal supervision. Our E2E KWS system consists of three sub-systems. The first sub-system is a recurrent neural network (RNN)-based acoustic auto-encoder trained to
Conventional keyword search (KWS) systems for speech databases match the input text query to the set of word hypotheses generated by an automatic speech recognition (ASR) system from utterances in the database. Hence, such KWS systems attempt to solve the complex problem of ASR as a precursor. Training an ASR system
Training a bottleneck feature (BNF) extractor with multilingual data has been common in low resource keyword search. In a low resource application, the amount of transcribed target language data is limited while there are usually plenty of multilingual data. In this paper, we investigated two methods to train
In this paper we propose a new technique for robust keyword spotting that uses bidirectional long short-term memory (BLSTM) recurrent neural nets to incorporate contextual information in speech decoding. Our approach overcomes the drawbacks of generative HMM modeling by applying a discriminative learning procedure
This paper proposes a novel system for robust keyword detection in continuous speech. Our decoder is composed of a bidirectional Long Short-Term Memory recurrent neural network using a Connectionist Temporal Classification (CTC) output layer, and a Dynamic Bayesian Network (DBN). The CTC network exploits bidirectional
This paper reports on investigations using two techniques for language model text data augmentation for low-resourced automatic speech recognition and keyword search. Lowresourced languages are characterized by limited training materials, which typically results in high out-of-vocabulary (OOV) rates and poor language
The recurrent neural network language model (RNNLM) is a discriminative, non-Markovian model that can capture long-span word history in natural language. It has been proved to be successful in automatic speech recognition and machine translation. In this work, we applied RNNLM to the n-best rescoring stage of the state-of-the-art BBN Byblos OCR (optical character recognition) system for handwriting...
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