A New Snr-feature Mapping for Robust Multistream Speech Recognition
نویسندگان
چکیده
We describe a new model of CASA labelling which assigns to each time-frequency region a probability "clean" enough to feed a multistream recogniser only adapted to clean data. This labelling process is based on the harmonicity of the speech. The probability is evaluated according to a SNR-feature mapping and the choice of a SNR decision threshold. This allows an extension of a previous method [1] based on the binary detection of noisy time-frequency regions, followed by partial recognition of clean regions. The labelling process is adapted to a new multistream recognition approach [5], since the previous probabilities serve to weight the streams' posteriors.
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روشی جدید در بازشناسی مقاوم گفتار مبتنی بر دادگان مفقود با استفاده از شبکه عصبی دوسویه
Performance of speech recognition systems is greatly reduced when speech corrupted by noise. One common method for robust speech recognition systems is missing feature methods. In this way, the components in time - frequency representation of signal (Spectrogram) that present low signal to noise ratio (SNR), are tagged as missing and deleted then replaced by remained components and statistical ...
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