the temporal modulation spectrum changes, and the intelligibility can be evaluated as a function of the temporal envelope cutoff frequency. Smearing of the temporal envelope should not be done on the wideband speech signal. It is known that those modulations can be reduced considerably, without affecting

shorter lifetime compared with CO2 (which has a lifetime of 100 years or more), a major focus on decreasing BC emissions offers an opportunity to mitigate the effects of global warming trends in the short term (see, for example, only the non-BC aerosols were controlled, it could potentially add 2.3 W m -2

speech � Beat spectrum � Spectral jitter In this paper, we investigate the use of jitter estimation over short time intervals (short-term jitter) for voice pathology detection in the case of running or continuous speech. Short-term jitter estimations are provided by the spectral jitter estimator

enabled by DBNs allows us to explicitly represent long-term articulatory and acoustic context in addition to the phonetic-state information maintained by hidden Markov models (HMMs). Furthermore, it enables us to model the short-term correlations among multiple observation streams within single time

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Linear Predictive (PLP) speech analysis technique [1] is based on the short-term spectrum of speech. Even though the short-term spectrum of speech is subsequently modified by several psychophysically based spectral transformations, the PLP technique (just like most other short-term spectrum based

was found to be as strong for the serial recognition as recall-based measures, favouring the view that it is phonological short-term memory capacity rather than speech output skills which constrain word learning. In Study 2, the association between phonological memory skills and vocabulary knowledge

utterances. Principal component analysis was used to analyze the importance of individual features in representing emotional categories. Three classification methods including vector quantization, artificial neural networks and Gaussian mixture density model were used. Classifications using short-term

Noise spectrum estimation is a fundamental component of speech enhancement and speech recognition systems. In this paper, we present an Improved Minima Controlled Recursive Averaging (IMCRA) approach, for noise estimation in adverse environments involving non-stationary noise, weak speech

(ASR) consists of a series of feature vectors, each vector representing the entire short-term frequency spectrum at a given time instant. Even one or a few corrupted elements in the feature vector lead to severe degradation of the recognition performance. Earlier work by Fletcher on articulatory index