We apply Fisher variate analysis to measure the e ectiveness of speaker normalization techniques. A trace criterion, which measures the ratio of the variations due to di erent phonemes compared to variations due to di erent speakers, serves as a rst assessment of a feature set without the need for recognition experiments. By using this measure and by recognition experiments we demonstrate that ...

It is well-known that the performance of automatic speech recognition (ASR) systems are easily affected by acoustic mismatch between training and testing conditions. This mismatch is often caused by various kinds of environmental noise or distortion. To reduce the effect of mismatch, feature normalization, feature enhancement, model adaptation, etc. have been studied intensively. Cepstral mean ...

Classical approaches for analyzing reverse transcription quantitative polymerase chain reaction (RT-qPCR) data commonly require normalization before assessing differential expression (DE). Normalization often has a substantial effect on the interpretation and validity of the subsequent analysis steps, but at the same time it causes a reduction in variance and introduces dependence among the nor...

The seven citation tones of the Southern Min dialect of Zhangzhou are described impressionistically, and a linguistic-tonetic representation of their acoustics derived from the z-score normalization of the tones of 9 male and 12 female speakers. A normalization of the raw mean tonal data is shown to be slightly superior to a log10 transform, delivering about an eight-fold reduction in the betwe...

Context is important for recovering language information from talker-induced variability in acoustic signals. In tone perception, previous studies reported similar effects of speech and nonspeech contexts in Mandarin, supporting a general perceptual mechanism underlying tone normalization. However, no supportive evidence was obtained in Cantonese, also a tone language. Moreover, no study has co...

Cepstral normalization has been popularly used as a powerful approach to produce robust features for speech recognition. Good examples of approaches include the well known Cepstral Mean Subtraction (CMS) and Cepstral Mean and Variance Normalization (CMVN), in which either the first or both the first and the second moments of the Mel-frequency Cepstral Coefficients (MFCCs) are normalized [1, 2]....

Text normalization transforms words into a base form so that terms from common equivalent classes match. Traditionally, information retrieval systems employ stemming techniques to remove derivational affixes. Depluralization, the transformation of plurals into singular forms, is also used as a low-level text normalization technique to preserve more precise lexical semantics of text. Experiment ...

Automatic speech recognizers perform poorly when training and test data are systematically different in terms of noise and channel characteristics. One manifestation of such differences is variations in the probability density functions (pdfs) between training and test features. Consequently, both automatic speech recognition and automatic speaker identification may be severely degraded. Previo...

The difference between training and testing environments is the major reason of performance degradation of speech recognition. In this paper, to further decrease the mismatch, we apply temporal filtering, Auto-Regression and Moving-Average (ARMA) filtering or RelAtive SpecTrAl (RASTA) filtering, as a post-processor for the log-Energy dynamic Range Normalization-Cepstral Mean and Variance Normal...