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Full-Text Articles in Signal Processing
Speaker Identification Using Usable Speech Concept, Ananth N. Iyer, Brett Y. Smolenski, Robert E. Yantorno, Jashmin K. Shah, Edward J. Cupples, Stanley J. Wenndt
Speaker Identification Using Usable Speech Concept, Ananth N. Iyer, Brett Y. Smolenski, Robert E. Yantorno, Jashmin K. Shah, Edward J. Cupples, Stanley J. Wenndt
Ananth N Iyer
Most signal processing involves processing a signal without concern for the quality or information content of that signal. In speech processing, speech is processed on a frame-by-frame basis, usually only with concern that the frame is either speech or silence. However, knowing how reliable the information is in a frame of speech can be very important and useful. This is where usable speech detection and extraction can play a very important role. The usable speech frames can be defined as frames of speech that contain higher information content compared to unusable frames with reference to a particular application. We have …
Robust Speaker Verification With Principal Pitch Components, Robert M. Nickel, Sachin P. Oswal, Ananth N. Iyer
Robust Speaker Verification With Principal Pitch Components, Robert M. Nickel, Sachin P. Oswal, Ananth N. Iyer
Ananth N Iyer
We are presenting a new method that improves the accuracy of text dependent speaker identification systems. The new method exploits a set of novel speech features that is derived from a principal component analysis (PC) of voiced speech segments. The new PC features are only weakly correlated with the corresponding cepstral features. A distance measure that combines both, cepstral and PC pitch features provides a discriminative power that cannot be achieved with cepstral features alone. It is well known that the discriminative power of cepstral features declines if the dimensionality of the feature space is increased beyond its optimal value. …
Sequential K-Nn Pattern Recognition For Usable Speech Classification, Jashmin K. Shah, Brett Y. Smolenski, Robert E. Yantorno, Ananth N. Iyer
Sequential K-Nn Pattern Recognition For Usable Speech Classification, Jashmin K. Shah, Brett Y. Smolenski, Robert E. Yantorno, Ananth N. Iyer
Ananth N Iyer
The accuracy of speech processing techniques degrades when operating in a co-channel environment. Co-channel speech occurs when more than one person is talking at the same time. The idea of usable speech segmentation is to identify and extract those portions of co-channel speech that are minimally degraded but still useful for speech processing application such as speaker identification. Usable speech measures are features that are extracted from the co-channel signal to distinguish between usable and unusable speech. In this paper, a new usable speech extraction technique is presented. The new method extracts features recursively and variable length segmentation is performed …
Usable Speech Detection Using A Context Dependent Gaussian Mixture Model Classifier, Robert E. Yantorno, Brett Y. Smolenski, Ananth N. Iyer, Jashmin K. Shah
Usable Speech Detection Using A Context Dependent Gaussian Mixture Model Classifier, Robert E. Yantorno, Brett Y. Smolenski, Ananth N. Iyer, Jashmin K. Shah
Ananth N Iyer
Speech that is corrupted by nonstationary interference, but contains segments that are still usable for applications such as speaker identification or speech recognition, is referred to as "usable" speech. A common example of nonstationary interference occurs when there is more than one person talking at the same time, which is known as co-channel speech. In general the above speech processing applications do not work in co-channel environments; however, they can work on the extracted usable segments. Unfortunately, currently available usable speech measures only detect about 75% of the total available usable speech. The first reason for this high error stems …