The aim of this study is to investigate the effect of Adaptive Dynamic Range Optimization (ADRO) on speech identification for cochlear implant (CI) users in adverse listening conditions. In this study, anechoic quiet, noisy, reverberant, noisy reverberant, and reverberant noisy conditions are evaluated. Two scenarios are considered when modeling the combined effects of reverberation and noise: ...

In this paper, we develop a novel modeling scheme for discrete-mixture HMMs (DMHMMs) by using maximum a posteriori (MAP) estimation. Also the MAP estimated DMHMMs are used for speech recognition to improve the accuracy under noisy conditions. The DMHMMs were originally proposed to reduce calculation costs in decoding process [1][2]. We propose a new method for MAP estimation of DMHMM parameters...

This paper presents a generalization of Rose's Integrated Parametric Model to the gaussian mixture hidden Markov model (HMM), formulation. Observations from clean speech HMM and noise HMM models are combined in the log spectra domain, through a corruption function, to generate noisy speech observations. In order to recognize noisy speech with the proposed model, when only the clean speech HMM a...

While spoken language translation remains a research goal, a crude form of it is widely available commercially for Japanese–English as a pipeline concatenation of speech-to-text recognition (SR), text-to-text translation (MT) and text-to-speech synthesis (SS). This paper proposes and illustrates an evaluation methodology for this noisy channel which tries to quantify the relative amount of degr...

In this paper, we apply the noise adaptive speech recognition for noisy speech recognition in non-stationary noise to the situation that acoustic models are trained from noisy speech. We justify it by that the noise adaptive speech recognition includes iterative processes between a noise parameter estimation step and a model adaptation step, which can possibly do non-linear mapping between the ...

Spectral subtraction (SS) is derived using maximum likelihood estimation assuming both noise and speech follow Gaussian distributions and are independent from each other. Under this assumption, noisy speech, speech contaminated by noise, also follows a Gaussian distribution. However, it is well known that noisy speech observed in real situations often follows a heavytailed distribution, not a G...

Example-based speech enhancement is a promising approach for coping with highly non-stationary noise. Given a noisy speech input, it first searches in noisy speech corpora for the noisy speech examples that best match the input. Then, it concatenates the clean speech examples that are paired with the matched noisy examples to obtain an estimate of the underlying clean speech component in the in...

Neural network can be used to “remember” speech patterns by encoding speech statistical regularity in network parameters. Clean speech can be “recalled” when noisy speech is input to the network. Adding more hidden layers can increase network capacity. But when the hidden layer size increases (deep network), the network is easily to be trapped to a local solution when traditional training strat...

Time-varying filtering of noisy speech signals based on linear prediction (LP) is presented. This approach is tested on noisy speech signals apparent in hands-free telephone systems.