Browsing by Subject "Triphones"

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Open Access
Large vocabulary speech recognition in noisy environments
(1998) Jabloun, Firas
A ІКПѴ set of speech feature parameters based on multirate subband analysis and the Teager Energy Operator (TEO) is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by logcompression and inverse DOT computation. The new feature parameters (TEOCEP) have a robust speech recognition performance in car engine noise which has a low pass nature. In this thesis, we also present some solutions to the problem of large vocabulary speech recognition. Triphone-based Hidden Markov. Models (HMM) are used to model the vocabulary words. Although the straight forward parallel search strategy gives good recognition performance, the processing time required is found to be long and impractical. Therefore another search strategy with similar performance is described. Subvocabularies are developed during the training session to reduce the total number of words considered in the search process. The search is then performed in a tree structure by investigating one subvocabulary instead of all the words.
Open Access
A large vocabulary speech recognition system for Turkish
(1999) Yılmaz, Cemal
This thesis presents a large vocabulary isolated word speech recognition system for Turkish. The triphones modeled by three-state Hidden Markov Models (HMM) are used as the smallest unit for the recognition. The HMM model of a word is constructed by using the HMM models of the triphones which make up the word. In the training stage, the word model is trained as a whole and then each HMM model of the triphones is extracted from the word model and it is stored individually. In the recognition stage, HMM models of triphones are used to construct the HMM models of the words in the dictionary. In this way, the words that are not trained can be recognized in the recognition stage. A new dictionary model based on trie structure is introduced for Turkish with a new search strategy for a given word. This search strategy performs breadth-first traversal on the trie and uses the appropriate region of the speech signal at each level of the trie. Moreover, it is integrated with a pruning strategy to improve both the system response time and recognition rate.