Mixture density HMMs with two-level transition.

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

Training mixture density HMMs with SOM and LVQ

The objective of this paper is to present experiments and discussions of how some neural network algorithms can help the phoneme recognition with mixture density hidden Markov models (MDHMMs). In MDHMMs the modeling of the stochastic observation processes associated with the states is based on the estimation of the probability density function of the short-time observations in each state as a m...

متن کامل

SOM based density function approximation for mixture density HMMs

This paper explains how some properties of the Self-Organizing Maps (SOMs) can be exploited in the density models used in continuous density hidden Markov models (HMMs). The three main ideas are the suitable initialization of the centroids for the Gaussian mixtures, the smoothing of the HMM parameters and the use of topology for fast density approximations. The methods are tested here in the au...

متن کامل

Segmental Lvq Training for Phoneme Wise Tied Mixture Density Hmms

This work presents training methods and recogni tion experiments for phoneme wise tied mixture den sities in hidden Markov models HMM The system trains speaker dependent but vocabulary independent phoneme models for the recognition of Finnish words The Learning Vector Quantization LVQ methods are applied to increase the discrimination between the phoneme models A segmental LVQ training is pro p...

متن کامل

Comparison results for segmental training algorithms for mixture density HMMs

This work presents experiments on four segmental training algorithms for mixture density HMMs. The segmental versions of SOM and LVQ3 suggested by the author are compared against the conventional segmental K-means and the segmental GPD. The recognition task used as a test bench is the speaker dependent, but vocabulary independent automatic speech recognition. The output density function of each...

متن کامل

Noisy speech recognition by using output combination of discrete-mixture HMMs and continuous-mixture HMMs

This paper presents an output combination approach for noiserobust speech recognition. The aim of this work is to improve recognition performance for adverse conditions which contain both stationary and non-stationary noise. In the proposed method, both discrete-mixture HMMs (DMHMMs) and continuous-mixture HMMs (CMHMMs) are used as acoustic models. In the DMHMM, subvector quantization is used i...

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: Journal of the Acoustical Society of Japan (E)

سال: 1993

ISSN: 0388-2861,2185-3509

DOI: 10.1250/ast.14.279