Two-stage Continuous Speech Recog Models: a Prelimin
نویسندگان
چکیده
In recent research, we have demonstrated that linguistic features can be used to improve speech recognition for an isolated vocabulary recognition task. This paper addresses two important new research problems in our attempts to build a two-stage speech recognition system using linguistic features. First, through a controlled study we show that our knowledge-driven feature sets perform competitively when compared with similar classes discovered by data-driven approaches. Secondly, we show that the cohort idea can be effectively generalized to continuous speech. Improved recognition results are achieved using this two-stage framework on multiple speech recognition experiments, on conversational telephone quality speech.
منابع مشابه
Improved 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 کاملTowards multi-domain speech understanding using a two-stage recognizer
This paper describes our eeorts in designing a two-stage recognizer with the objective of developing a multi-domain speech understanding system. We envisage one rst-stage recognition engine that is domain-independent, and multiple second-stage systems specializing in individual domains. A major novelty in our initial two-stage design is a front-end that incorporates angie-based hierarchical sub...
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 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 کاملPhoneme recognition in continuous speech using large inhomogeneous hidden Markov models
In this paper we present a novel scheme for phoneme recognition in continuous speech using inhomogeneous hidden Markov models (IHMMs). IHMMs can capture the temporal structure of phonemes and inter-phonemic temporal relationships effectively, with their duration dependent state transition probabilities. A two stage IHMM is proposed to capture the variabilities in speech effectively for phoneme ...
متن کامل