In mobile speech communication, adverse conditions, such as noisy acoustic environments and unreliable network connections, may severely degrade the intelligibility and naturalness of the received speech quality, and increase the listening effort. This thesis focuses on countermeasures based on statistical signal processing techniques. The main body of the thesis consists of three research arti...

In this paper, we introduced a new framework of speech recognizer based on HMM and neural net. Unlike the traditional hybrid system, the neural net was used as a post processor, which classify the speech data segmented by HMM recognizer. The purpose of this method is to improve the top-choice accuracy of HMM based speech recognition system in our lab. Major issues such as how to use the segment...

In the state-of-the-art automatic speech recognition (ASR) systems, adaption techniques are used to the mitigate performance degradation caused by the mismatch in the training and testing procedure. Although there are bunch of adaption techniques for the hidden Markov models (HMM)-GMM-based system[3], there is rare work about the adaption in the hybrid artificial neural network (ANN)/HMM-based ...

ABSTRACT We present an algorithm to derive 7 kHz wideband speech from narrowband “telephone speech”. A statistical approach is used that is based on a Hidden Markov Model (HMM) of the speech production process. A new method for the estimation of the wideband spectral envelope is proposed, using nonlinear statespecific techniques to minimize a mean square error criterion. In contrast to common m...

In this paper, we describe a novel acoustic model adaptation technique which generates “speaker-independent” HMM for the target environment. Recently, personal digital assistants like cellular phones are shifting to IP terminals. The encoding-decoding process utilized for transmitting over IP networks deteriorates the quality of speech data. This deterioration causes degradation in speech recog...