Showing papers on "Intelligent word recognition published in 1981"

Isolated and Connected Word Recognition--Theory and Selected Applications

Abstract: The art and science of speech recognition have been advanced to the state where it is now possible to communicate reliably with a computer by speaking to it in a disciplined manner using a vocabulary of moderate size. It is the purpose of this paper to outline two aspects of speech-recognition research. First, we discuss word recognition as a classical pattern-recognition problem and show how some fundamental concepts of signal processing, information theory, and computer science can be combined to give us the capability of robust recognition of isolated words and simple connected word sequences. We then describe methods whereby these principles, augmented by modern theories of formal language and semantic analysis, can be used to study some of the more general problems in speech recognition. It is anticipated that these methods will ultimately lead to accurate mechanical recognition of fluent speech under certain controlled conditions.

Isolated word recognition using a two-pass pattern recognition approach

Abstract: One of the major drawbacks of the standard pattern recognition approach to isolated word recognition is that poor performance is generally achieved for word vocabularies with acoustically similar words. This poor performance is related to the pattern similarity (distance) algorithms that are generally used in which a global distance between the test pattern and each reference pattern is computed. Since acoustically similar words are, by definition, globally similar, it is difficult to reliably discriminate such words, and a high error rate is obtained. By modifying the pattern similarity algorithm so that the recognition decision is made in two passes, improvements in discriminability among similar words can be achieved. In particular, on the first pass the recognizer provides a set of global distance scores which are used to decide a class (or a set of possible classes) in which the spoken word is estimated to belong. On the second pass a locally weighted distance is used to provide optimal separation among words in the chosen class (or classes) and the recognition decision is made on the basis of these local distance scores. For a highly complex vocabulary (letters of the alphabet, digits, and 3 command words) recognition improvements of from 3 to 7 percent were obtained using the two-pass recognition strategy.

Experiments on an isolated-word recognition system for multiple speakers

Abstract: The paper describes isolated-word recognition experiments on a multi-speaker speech recognition system. The system is organized in two main stages. At the phonemic recognition stage the phonemic transcription of the speech waveform is produced by simultaneous segmentation and labeling accomplished by the Learning Subspace Method. It directly produces an approximately correct number of phonemes. At the word recognition stage Redundant Hash Addressing is used for fast comparison of the phonemic transcriptions with referent strings stored in a dictionary. The average word recognition accuracy in a 200-word experiment with five speakers was about 95 per cent.