Phoneme Modeling for Speech Recognition in Kannada using Multivariate Bayesian Classifier

نویسندگان
چکیده

برای دانلود رایگان متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Phoneme Modeling for Speech Recognition in Kannada using Multivariate Bayesian Classifier

We build an automatic phoneme recognition system based on Bayesian Multivariate Modeling which is a static scheme. Phoneme models were built by using stochastic pattern recognition and acoustic phonetic schemes to recognise phonemes. Since our native language is Kannada, a rich South Indian Language, we have used 15 Kannada phonemes to train and test these models. As Mel – Frequency Cepstral Co...

متن کامل

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 کامل

Allophone-based acoustic modeling for Persian phoneme recognition

Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects o...

متن کامل

Bayesian Acoustic Modeling for Spontaneous Speech Recognition

Accurate acoustic model construction for spontaneous speech recognition requires that various speech fluctuation factors such as speaking variations and speaker variances are dealt with. The Bayesian approach has advantages for the speech fluctuation modeling because it enables an appropriate model selection for given speech data, unlike the maximum likelihood approach. However, the Bayesian ap...

متن کامل

Improving Phoneme Sequence Recognition using Phoneme Duration Information in DNN-HSMM

Improving phoneme recognition has attracted the attention of many researchers due to its applications in various fields of speech processing. Recent research achievements show that using deep neural network (DNN) in speech recognition systems significantly improves the performance of these systems. There are two phases in DNN-based phoneme recognition systems including training and testing. Mos...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

ژورنال

عنوان ژورنال: International Journal of Electronics and Communication Engineering

سال: 2014

ISSN: 2348-8549

DOI: 10.14445/23488549/ijece-v1i9p101