New developments in the INRS continuous speech recognition system
نویسندگان
چکیده
New techniques are developed for the second pass search in our large vocabulary continuous speech recognition system. The merging of recognition hypotheses is proposed in order to linearize the exponential growth of the tree structure in the depth rst search. Branching ordering of the rst pass word graph and pruning at both word and phone levels are used to further speed up the search. The algorithm has been shown to be e ective on the speaker-independent WSJ task.
منابع مشابه
Speaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملSpeaker Adaptation in Continuous Speech Recognition Using MLLR-Based MAP Estimation
A variety of methods are used for speaker adaptation in speech recognition. In some techniques, such as MAP estimation, only the models with available training data are updated. Hence, large amounts of training data are required in order to have significant recognition improvements. In some others, such as MLLR, where several general transformations are applied to model clusters, the results ar...
متن کاملA Database for Automatic Persian Speech Emotion Recognition: Collection, Processing and Evaluation
Abstract Recent developments in robotics automation have motivated researchers to improve the efficiency of interactive systems by making a natural man-machine interaction. Since speech is the most popular method of communication, recognizing human emotions from speech signal becomes a challenging research topic known as Speech Emotion Recognition (SER). In this study, we propose a Persian em...
متن کاملImproved Bayesian Training for Context-Dependent Modeling in Continuous Persian Speech Recognition
Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of context-dependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven clust...
متن کاملRobust gender-dependent acoustic-phonetic modelling in continuous speech recognition based on a new automatic male/female classification
In this paper we present a new automatic male/female classi cation method based on the location in the frequency domain of the rst 2 formants. This classi cation is based on a new automatic formant extraction which is faster than a peak picking technique. Gender-dependent acoustic-phonetic models stemming from this classi cation are used in the INRS Continuous speech recognition system with ATI...
متن کامل