Look-ahead sequential feature vector normalization for noisy speech recognition
نویسنده
چکیده
Cepstral mean subtraction (CMS), which is a simple long-term bias removal, is used to compensate for transmission and linear xed channel e ects. In order to process the non-linear channel, a two-level CMS was proposed where separate channel compensation is performed for segments that are classi ed as speech and for segments classied as background. In this paper, methods for extending the two-level CMS to real-time implementation is proposed using a nite number of look-a-head frame delay, which further reduces computation and memory requirements of the compensation process. The on-line bias compensation shows similar characteristic curve as that of batch-mode and has the e ect of greatly reducing the sensitivity of the recognizer to transmission noise variability.
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