In this paper, we propose a feature enhancement algorithm for wireless speech recognition in adverse acoustic environments. A speech recognition system is realized at the receiver side of a wireless communications system and feature parameters are extracted directly from the bitstream of the speech coder employed in the system. The feature parameters are composed of spectral envelope and coder-...

We have proposed new approach for the speech recognition system by applying kernel adaptive filter for speech enhancement and for the recognition, the hybrid HMM/DTW methods are used in this paper. Noise removal is very important in many applications like telephone conversation, speech recognition, etc. In the recent past, the kernel methods are showing good results for speech processing applic...

This paper presents a novel approach to robust estimation of linear prediction (LP) model parameters in the application of speech enhancement. The robustness stems from the use of prior knowledge on the clean speech and the interfering noise, which are represented by two separate codebooks of LP model parameters. We propose to model the temporal dependency between short-time model parameters wi...

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech, we assume that speech is the output of a nonlinear prediction HMM (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parame...

This paper describes a method of enhancing speech corrupted by additive uncorrelated noise. The approach adopted is to use cepstral-domain hidden Markov models to determine statistics of the clean speech and noise processes. A compensated model of speech corrupted by noise is generated using parallel model combination. MMSE and linear non-homogeneous estimators of the clean speech signal are de...

General speaker-independent models have been used in nonnegative matrix factorization (NMF) based speech enhancement algorithms for the practical applicability. And additional regulation is necessary when choosing the optimal models for speech reconstruction. In this paper, we propose a novel utilization of deep neural network (DNN) to select the models used for separating speech from noise. Sp...

Speech is one of the ways to express ourselves naturally. So, speech can be used as a means to communicate with machines. In this work, using MATLAB as a platform isolated word recognizer is achieved. Speech signals get distorted by many kinds of noises. Hence, it is necessary to reduce the noise contained in the speech signal. This is called speech enhancement. Speech enhancement aims at impro...

The use of visual features in the form of lip movements to improve the performance of acoustic speech recognition has been shown to work well, particularly in noisy acoustic conditions. However, whether this technique can outperform speech recognition incorporating well-known acoustic enhancement techniques, such as spectral subtraction, or multi-channel beamforming is not known. This is an imp...

In this paper, we propose new approaches to speech enhancement based on soft decision. In order to enhance the statistical reliability in estimating speech activity, we introduce the concept of a global speech absence probability (GSAP). First, we compute the conventional speech absence probability (SAP) and then modify it according to the newly proposed GSAP. The modification is made in such a...