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 network side of a wireless communications system and feature parameters are extracted directly from the bitstream of the speech coder employed in the system, where the feature parameters are composed of spectral envelope infor...

The most common mode of communication between humans is speech. As this is the most preferred way, humans would like to use speech to interact with machines also. That is why, speech recognition has gained a lot of popularity. Many approaches for speech recognition exist like Dynamic Time Warping (DTW), Hidden Markov Model (HMM). The main objective of this paper is defined a three stage neural ...

We propose a signal pre-processing front-end to enhance speech based on deep neural networks (DNNs) and use the enhanced speech features directly to train hidden Markov models (HMMs) for robust speech recognition. As a comprehensive study, we examine its effectiveness for different acoustic features, acoustic models, and training-testing combinations. Tested on the Aurora4 task the experimental...

Acoustical mismatch between training and testing phases induce degradation of performance in automatic speaker recognition systems [1,2]. Providing robustness to speaker recognizers has to be, therefore, a priority matter. Robustness in the acoustical stage can be accomplished through speech enhancement techniques as a prior stage to the recognizer. These techniques are oriented to the reductio...

Decision tree-based clustering and parameter estimation are essential steps in the training part of an HMM-based speech synthesis system. These two steps are usually performed based on the maximum likelihood (ML) criterion. However, one of the drawbacks of the ML criterion is that it is sensitive to outliers which usually result in quality degradation of the synthesized speech. In this letter, ...

As described in this paper, we propose a sub-band speech synthesis approach to develop a high quality Text-to-Speech (TTS) system: a sample-based spectrum is used in the high-frequency band and spectrum generated by HMM-based TTS is used in the low-frequency band. Herein, sample-based spectrum means spectrum selected from a phoneme database such that it is the most similar to spectrum generated...

Performance of speech recognition systems strongly degrades in the presence of background noise, like the driving noise inside a car. In contrast to existing works, we aim to improve noise robustness focusing on all major levels of speech recognition: feature extraction, feature enhancement, speech modelling, and training. Thereby, we give an overview of promising auditory modelling concepts, s...