A new speech intelligibility improvement method for near-end listening enhancement in noisy environments is proposed. This method improves speech intelligibility by optimizing energy correlation of one-third octave bands of clean speech and enhanced noisy speech without power increasing. The energy correlation is determined as a cost function based on frequency band gains of the clean speech. I...

This paper proposes a new method using piecewise-linear transformation for adapting phone HMMs to noisy speech. Various noises are clustered according to their acoustical property and signal-to-noise ratios (SNRs), and noisy speech HMM corresponding to each clustered noise is made. Based on the likelihood maximization criterion, the HMM which best matches an input speech is selected and further...

Conventional speech enhancement methods typically utilize the noisy phase spectrum for signal reconstruction. This letter presents a novel method to estimate the clean speech phase spectrum, given the noisy speech observation in single-channel speech enhancement. The proposed method relies on the phase decomposition of the instantaneous noisy phase spectrum followed by temporal smoothing in ord...

In this paper, we describe a new speech/non-speech classification method that improves the endpoint detection performance for speech recognition in noisy environments. The proposed method uses multiple features to increase the robustness in noisy environments, and the classification and regression tree(CART) technique is applied to effectively combine these multiple features for classification ...

Recognition performance decreases when recognition systems are used over the telephone network, especially wireless network and noisy environments. It appears that non efficient speech/non-speech detection is a very important source of this degradation. Therefore, speech detector robustness to noise is a challenging problem to be examined, in order to improve recognition performance for the ver...

This paper describes a new program, correct, which takes words rejected by the Unix spell program, proposes a list of candidate corrections, and sorts them by probability. The probability scores are the novel contribution of this work. Probabilities are based on a noisy channel model. It is assumed that the typist knows what words he or she wants to type but some noise is added on the way to t...