This paper is concerned with the problem of phonetic modeling in a Mandarin keyword spotting system. The task is to detect 20 keywords from continuous speech in the Call Home corpus from the Linguistic Data Consortium (LDC). Different speech units are explored, including whole word, syllable, and demi-syllable (INITIAL and FINAL). In our speaker-independent HMM-based Mandarin keyword spotting e...

Keyword Spotting is a well-known method in document image retrieval. In this method, Search in document images is based on query word image. In this Paper, an approach for document image retrieval based on keyword spotting has been proposed. In proposed method, a framework using relevance feedback is presented. Relevance feedback, an interactive and efficient method is used in this paper to imp...

This paper proposes a method of predicting accuracy of keyword spotting in terms of FA count and spotting score of correct detections. A new measure F for predicting the FA count is calculated by simulation of the keyword spotting for phoneme sequences that phoneme-based language model generates. Another measure C for predicting the spotting score of correct detections is obtained from a produc...

Word spotting, or keyword identification, is a highly challenging task when there are multiple speakers speaking simultaneously. In the case of a game being controlled by children solely through voice, the task becomes extremely difficult. Children, unlike adults, typically do not await their turn to speak in an orderly fashion. They interrupt and shout at arbitrary times, speak or say things t...

We propose a single neural network architecture for two tasks: on-line keyword spotting and voice activity detection. We develop novel inference algorithms for an end-to-end Recurrent Neural Network trained with the Connectionist Temporal Classification loss function which allow our model to achieve high accuracy on both keyword spotting and voice activity detection without retraining. In contr...

This paper presents and discusses keyword spotting methods for searching in speech. In contrast with searching in text, the searching in speech or generally in multimedia data still represents a challenge. The aim of the paper is to present a keyword spotting (KWS) method based on a large vocabulary continuous speech recognition (LVCSR) system, based on phonetics decoder, and keyword spotting u...

Keyword Spotting Using Normalization of Posterior Probability Confidence Measures by Rachna Vijay Vargiya Thesis Advisor: Marius C. Silaghi, Ph.D. Keyword spotting techniques deal with recognition of predefined vocabulary keywords from a voice stream. This research uses HMM based keyword spotting algorithms for this purpose. The three most important componenets of a keyword detection system are...

Keyword retrieval in handwritten document images (word spotting) is very challenging given that OCR accuracy is not yet adequate for handwritten scripts, specially with large lexicons. Various proposed approaches build indices on information such as image features or OCR scores and have improved the performance of the traditional approach that builds index on OCR’ed text. In this paper, we impr...

This paper proposes a new approach for keyword spotting, which is not based on HMMs. The proposed method employs a new discriminative learning procedure, in which the learning phase aims at maximizing the area under the ROC curve, as this quantity is the most common measure to evaluate keyword spotters. The keyword spotter we devise is based on nonlinearly mapping the input acoustic representat...