Lexicon-based handwritten text keyword spotting (KWS) has proven to be a very fast and accurate alternative to lexicon-free methods. Nevertheless, since lexicon-based KWS methods rely on a predefined vocabulary, fixed in the training phase, they perform poorly for any query keyword that was not included in it (i.e. out-of-vocabulary keywords). This turns the KWS system useless for that particul...

In this paper, we propose a context-aware keyword spotting model employing a character-level recurrent neural network (RNN) for spoken term detection in continuous speech. The RNN is end-toend trained with connectionist temporal classification (CTC) to generate the probabilities of character and word-boundary labels. There is no need for the phonetic transcription, senone modeling, or system di...

This research extends our earlier work on using machine translation (MT) and word-based recurrent neural networks to augment language model training data for keyword search in conversational Cantonese speech. MT-based data augmentation is applied to two language pairs: English-Lithuanian and English-Amharic. Using filtered N-best MT hypotheses for language modeling is found to perform better th...

A basic problem in keyword spotting is the fact that the keywords itself cannot be completely different from background speech. Therefore, false alarms arise from those parts of the keyword which are also contained in the background. The paper describes the favourable application of a model trellis which enables to test individual phoneme sequences with respect to their influence on the underly...

In this paper, we present a novel hybrid keyword spotting system that combines supervised and semi-supervised competitive learning algorithms. The rst stage is a S-SOM (Semi-supervised SelfOrganizing Map) module which is speci cally designed for discrimination between keywords (KWs) and non-keywords (NKWs). The second stage is an FDVQ (Fuzzy Dynamic Vector Quantization) module which consists of...

In this paper, orthogonal transform-based signal bias removal (OTSBR) approach and RNN prosodic model are proposed for multi-keyword spotting of telephone speech. OTSBR is employed in the pre-processing stage of acoustic decoding and aimed at channel bias estimation to eliminate the acoustic mismatch between training and testing environments. The RNN prosodic model is adopted in the post-proces...

In this paper, we develop a keyword spotting system using vocabulary-independent speech recognition technique, and investigate several non-keyword modeling methods to improve its performance. In order to overcome the weakness of conventional syllable model, we propose the syllable filler based on syllable information of keywords and syllable-like filler model. The former prohibits syllable fill...

In this paper, a novel approach for the design of cohort models for word spotting in continuous speech is presented. This new approach is based on modifying the probability density function of a conventional filler so that regions in the feature space that are related to the keyword will be reduced or removed. By modifying these regions, the filler and keyword models become more orthogonal in t...

Keyword Spotting (KWS) systems developed for low resource languages with very little transcribed audio suffer due to a small vocabulary (high out-of-vocabulary (OOV) rate) and a weak language model. In this paper, we propose to augment such systems using automatically retrieved web documents. Our procedure can find large volumes of web documents similar to a small pool of training transcription...

Speech is perhaps the most private form of personal communication but current speech processing techniques are not designed to preserve the privacy of the speaker and require complete access to the speech recording. We propose to develop techniques for speech processing which do preserve privacy. While our proposed methods can be applied to a variety of speech processing problems and also gener...