In this paper, we describe our effort in developing new method of false alarm rejection for keyword spotting type of speech recognition system. This false alarm rejection uses prosodic similarities, and works as posterior rescore basis. In keyword spotting, there is always false alarm problem. Here, we propose a technique to reject those false alarms using prosodic features. In Japanese, prosod...

In this letter, we propose a novel approach to estimate three different kinds of phone mismatch penalty matrices for two-stage keyword spotting. When the output of a phone recognizer is given, detection of a specific keyword is carried out through text matching with the phone sequences provided by the specified keyword using the proposed phone mismatch penalty matrices. The penalty matrices ass...

This paper describes several approaches to keyword spotting (KWS) for informal continuous speech. We compare acoustic keyword spotting, spotting in word lattices generated by large vocabulary continuous speech recognition and a hybrid approach making use of phoneme lattices generated by a phoneme recognizer. The systems are compared on carefully defined test data extracted from ICSI meeting dat...

With the rapid developments of wireless communications, it is highly desired for users to access the network information with spoken dialogue interface via hand-held devices at any time, from anywhere. One possible approach towards this goal is to perform speech feature extraction at the hand-held devices (the clients) and have all other recognition tasks and dialogue functions absorbed by the ...

The emergence of artificial intelligence (AI) driven keyword spotting (KWS) technologies has revolutionized human to machine interaction. Yet, the challenge end-to-end energy efficiency, memory footprint and system complexity current neural network (NN) powered AI-KWS pipelines remained ever present. This paper evaluates KWS utilizing a learning automata algorithm called Tsetlin Machine (TM). T...

Retrieval of text information from natural scene images and video frames is a challenging task due to its inherent problems like complex character shapes, low resolution, background noise, etc. Available OCR systems often fail to retrieve such information in scene/video frames. Keyword spotting, an alternative way to retrieve information, performs efficient text searching in such scenarios. How...

Support Vector Machines (SVM) is one such machine learning technique that learns the decision surface through a process of discrimination and has a good generalization capacity [6]. SVMs have been proven to be successful classifiers on several classical pattern recogntion problems [9, 11]. In this paper, one of the first applications of Support Vector Machines (SVM) technique for the problem of...

This paper describes our complete results for improved lexical llers as well as two new kinds of llers, gives their results in unlimited speech recognition as well as for keyword spotting and compares them to the acoustic-phonetic ller in the case of keyword spotting. Tests have been conducted on di erent vocabularies derived from ATIS and the Wall Street Journal database. Results for keyword s...

Searching documents for information and retrieval of relevant documents is a basic activity. Various tools are readily available for searching and retrieval from digital documents, but not much robust methods are available for retrieval from historic documents and old manuscripts as they are not digitized but available in scanned formats. Conventional way of retrieval from scanned document imag...

Speech recognition systems are prone to severe degradation in noisy environments due to mismatch between training and testing conditions. A multi-stream approach for keyword spotting is proposed to improve robustness in mismatched conditions. The assumption is that most real world noises are colored and do not affect the full spectrum equally, meaning certain parts of the spectrum can still pro...