Showing papers on "Signature recognition published in 1986"

Script recognition using hidden Markov models

Abstract: A handwritten script recognition system is presented which uses Hidden Markov Models (HMM), a technique widely used in speech recognition. The script is encoded as templates in the form of a sequence of quantised inclination angles of short equal length vectors together with some additional features. A HMM is created for each written word from a set of training data. Incoming templates are recognised by calculating which model has the highest probability for producing that template. The task chosen to test the system is that of handwritten word recognition, where the words are digits written by one person. Results are given which show that HMMs provide a versatile pattern matching tool suitable for some image processing tasks as well as speech processing problems.

On hidden Markov models in isolated word recognition

Abstract: Hidden Markov modeling has become an increasingly popular technique in automatic speech recognition. Recently, attention has been focused on the application of these models to talker-independent, isolated-word recognition. Initial results using models with discrete output densities for isolated-digit recognition were later improved using models based on continuous output densities. In a series of experiments on isolated-word recognition, we applied hidden Markov models with multivariate Gaussian output densities to the problem. Speech data was represented by feature vectors consisting of eight log area ratios and the log LPC error. A weak measure of vocal-tract dynamics was included in the observations by appending to the feature vector observed at time t, the vector observed at time t-δ, for some fixed offset δ. The best models were obtained with offsets of 75 or 90 msecs. When a comparison is made on a common data base, the resulting error rate of 0.2% for isolated-digit recognition improves on previous algorithms.

Isolated word recognition based on finite-state vector quantization

Abstract: In this paper, we propose an isolated word recognition system based on the finite-state vector quantization (FSVQ) method. The recognition system can be viewed as a finite state machine composed of a codebook and next-state functions. As compared to an isolated word recognition system that uses the conventional memoryless vector quantization, the proposed system requires far less search time, and needs no segmentation of input speech, yet yields comparable recognition accuracies. For the design of next-state functions, two techniques, that is, the conditional histogram and omniscient design methods, are used, and their performances are compared in recognition of the ten Korean digits.

Connected word recognition by overlap and split of reference patterns and its performance evaluation tests

Abstract: This paper proposes a new method for connected word recognition. To reduce pattern matching errors which are caused by coarticulation, talking rate variation, and silence between words in continuous speech, overlap and splitting of reference patterns and normalization of accumulated distances are introduced into our basic recognition algorithm. Also new algorithms to reduce the amount of computation and to train the recognition system are proposed. Performance evaluation tests show the efficiency of these algorithms and the entire method. Dominant factors of speech data influencing recognition performance are also investigated.