Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...

We propose an approach to reverberant speech recognition adopting deep learning in front end as well as back end of the system. At the front end, we adopt a deep autoencoder for enhancing the speech feature parameters, and the recognition is performed using a DNN-HMM acoustic models trained on multi-condition data. The system was evaluated through the ASR task in Chime Challenge 2014. The DNN-H...

We present a new feature representation for speech recognition based on both amplitude modulation spectra (AMS) and frequency modulation spectra (FMS). A comprehensive modulation spectral (CMS) approach is defined and analyzed based on a modulation model of the band-pass signal. The speech signal is processed first by a bank of specially designed auditory band-pass filters. CMS are extracted fr...

We address issues for improving handsfree speech recognition performance in different car environments using a single distant microphone. In this paper, we propose a nonlinear multiple-regression-based enhancement method for in-car speech recognition. In order to develop a data-driven in-car recognition system, we develop an effective algorithm for adapting the regression parameters to differen...

In this paper, a gain-adapted speech recognition in unknown noise is developed in time domain. The noise is assumed to be the colored noise. The nonstationary autoregressive (NAR) hidden markov model (HMM) used to model clean speeches, The nonstationary AR is modeled by polynomial functions with a linear combination of A4 known basis functions. Enhancement using multiple Kalman filters is perfo...

In this paper, we propose a model integration method for hidden Markov model (HMM) and deep neural network (DNN) based acoustic models using a product-of-experts (PoE) framework in statistical parametric speech synthesis. In speech parameter generation, DNN predicts a mean vector of the probability density function of speech parameters frame by frame while keeping its covariance matrix constant...

It is possible to increase the intelligibility of speech in noise by enhancing the clean speech signal. In this paper we demonstrate the effects of modifying the spectral envelope of synthetic speech according to the environmental noise. To achieve this, we modify Mel cepstral coefficients according to an intelligibility measure that accounts for glimpses of speech in noise: the Glimpse Proport...

In the multiple-model based speech recognition system, multiple HMM models corresponding to different types of noise signals and SNR values are trained and the one model which is most close to the input speech is selected for recognition. In the previous research on the multiplemodel based speech recognition, it has been thought that the best performance can be obtained by selecting the HMM mod...