Showing papers on "Intelligent word recognition published in 1984"
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01 Mar 1984TL;DR: This paper describes the speaker-independent spoken word recognition system for a large size vocabulary and results are obtained for the training samples in the 212 words uttered by 10 male and 10 female speakers.
Abstract: This paper describes the speaker-independent spoken word recognition system for a large size vocabulary. Speech is analyzed by the filter bank, from whose logarithmic spectrum the 11 features are extracted every 10 ms. Using the features the speech is first segmented and the primary phoneme recognition is carried out for every segment using the Bayes decision method. After correcting errors in segmentation and phoneme recognition, the secondary recognition of part of the consonants is carried out and the phonemic sequence is determined. The word dictionary item having maximum likelihood to the sequence is chosen as the recognition output. The 75.9% score for the phoneme recognition and the 92.4% score for the word recognition are obtained for the training samples in the 212 words uttered by 10 male and 10 female speakers. For the same words uttered by 30 male and 20 female speakers different from the above speakers, the 88.1% word recognition score is obtained.
7 citations
01 Jan 1984
3 citations
01 Jan 1984
TL;DR: In this paper, a speaker-independent spoken word recognition system for a large size vocabulary is described, in which speech is analyzed by the filter bank, from whose logarithmic spectrum the 11 features are extracted every 10 ms.
Abstract: This paper describes the speaker-independent spoken word recognition system for a large size vocabulary. Speech is analyzed by the filter bank, from whose logarithmic spectrum the 11 features are extracted every 10 ms. Using the features the speech is first segmented and the primary phoneme recognition is carried out for every segment using the Bayes decision method. After correcting errors in segmentation and phoneme recognition, the secondary recognition of part of the consonants is carried out and the phonemic sequence is determined. The word dictionary item having maximum likelihood to the sequence is chosen as the recognition output. The 75.9% score for the phoneme recognition and the 92.4% score for the word recognition are obtained for the training samples in the 212 words uttered by 10 male and 10 female speakers. For the same words uttered by 30 male and 20 female speakers different from the above speakers, the 88.1% word recognition score is obtained.
3 citations
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01 Mar 1984TL;DR: This paper describes a syntax-directed real-time connected-word recognition system based on whole word template matching, and the recognition is done using a one-pass dynamic time-warping algorithm.
Abstract: This paper describes a syntax-directed real-time connected-word recognition system and compares the performance of different recognition algorithms. This is a speaker dependent system based on whole word template matching, and the recognition is done using a one-pass dynamic time-warping algorithm. The symmetric local constraints of Sakoe and Chiba are used for connected word recognition, and with proper normalization of the accumulated distances, result in better performance than the Itakura local constraints. By allowing a noise template to start and end the sentences during the recognition process, most of the errors due to a bad endpoint detection are eliminated. When memory and computational resources are limited, vector quantization allows more templates for each word in the dictionary, and as a consequence, the recognition performance increases. By carefully implementing the algorithm on standard hardware (VAX 11/780 and FPS AP-120B), real-time recognition is achieved for vocabularies of up to 100 templates, or up to 250 templates if vector quantization is used.
3 citations