We propose a technique for generating alternative models for keywords in a hybrid hidden Markov model artificial neural network (HMM-ANN) keyword spotting paradigm. Given a base pronunciation for a keyword from the lookup dictionary, our algorithm generates a new model for a keyword which takes into account the systematic errors made by the neural network and avoiding those models that can be c...

This paper presents our new keyword spotting system taking advantage of both the filler model and the confidence measure approaches. The novelty is in a non-standard connection of the filler and the keyword models together with introduction of a new confidence measure based on a keyword normalized score. In detail the paper deals with a decision block. Two methods are introduced. The first is b...

In telephone speech recognition, the acoustic mismatch between training and testing environments often causes a severe degradation in the recognition performance. This paper presents a keyword-driven two-level codebook-based stochastic matching (CBSM) algorithm to eliminate the acoustic mismatch. Additionally, in Mandarin speech, it is dicult to correctly recognize the unvoiced part in a sylla...

Many word-spotting applications require an open keyword vocabulary, allowing the user to search for any term in an audio document database. In conjunction with this, an automatic method of determining the acoustic representation of an arbitrary keyword is needed. For a HMMbased system, where the keyword is represented by a concatenated string of phones, the keyword phone string (KPS), the phone...

The task of keyword spotting is to detect a set of keywords in the input continuous speech. In a keyword spotter, not only the keywords, but also the non-keyword intervals must be modeled. For this purpose, filler (or garbage) models are used. To date, most of the keyword spotters have been based on hidden Markov models (HMM). More specifically, a set of HMM is used as garbage models. In this p...

It has been shown in [1, 2] that improved performance can be achieved by formulating the keyword spotting as a non-uniform error automatic speech recognition problem. In this work, we discriminatively train a deep bidirectional long short-term memory (BLSTM) hidden Markov model (HMM) based acoustic model with non-uniform boosted minimum classification error (BMCE) criterion which imposes more s...

Automatic speech recognition (ASR) technology is available now-a-days in all handsets where keyword spotting plays a vital role. Keyword spotting performance significantly degrades when applied to real-world environment due to background noise. As visual features are not affected much by noise this provides better solution. In this paper, audio-visual integration is proposed which combines audi...

With advances in the field of digitization of printed documents and several mass digitization projects underway, information retrieval and document search have emerged as key research areas. However, most of the current work in these areas is limited to English and a few oriental languages. The lack of efficient solutions for Indic scripts and languages such as Sanskrit has hampered information...

This paper presents a keyword spotting method based on searching a syllable lattice structure. The Mandarin syllables are represented in initial-final models. By one-stage dynamic programming, an utterance is converted into a sequence of topN-candidate syllables. It comes out a syllable lattice structure for this input utterance. A vocabulary of predefined keywords is represented as a set of sy...

Keyword spotting can be formulated as a non-uniform error automatic speech recognition (ASR) problem. It has been demonstrated [1] that this new formulation with the nonuniform MCE training technique can lead to improved system performance in keyword spotting applications. In this paper, we demonstrate that deep neural networks (DNNs) can be successfully trained on the non-uniform minimum class...