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Understanding the noise characteristics for finding appropriate filtering technique/s so as to obtain sufficiently clear speech samples for Speaker Identification, is one of the challenging tasks in Forensic Acoustics. Speaker's idiosyncratic speech should not be affected when the noise reduction is carried out; otherwise, Speaker Identification becomes highly erroneous. We have collected fifty noisy speech samples reported to be recorded in different modes from actual crime cases received in the laboratory. The samples are analyzed after subjecting to various filtering techniques and compared with the clear speech mixed with the noise collected from non-speech portion. Distortion levels on the speech are studied at various stages of application of filters in terms of SNR and Speaker Specific Information. Retaining the Speaker Specific Information as primary concern of our study, the limitation of filtering techniques depending on the characteristic and intensity level of noise is worked out for noisy speech samples. Subsequently a statistical study is also conducted. Listening tests were conducted to ensure that the perceptual features of the original noisy speech are preserved while applying filters. This work demonstrates the efficiency of Noise reduction filters in improving SNR and their controlled applications for preserving Speaker dependent features depending on the various noise characteristics embedded on speech samples. Audio Forensics has a challenging history of enhancement problems of speech samples received for examination. It is observed that out of the total speech samples received for Speaker Identification in the Laboratory, a large number of recordings requires enhancement. Speech is a non-linear time series represented in terms of complex number. Hence separating noise from noisy speech in spectral domain results into countless solutions. The main objective of a Noise Cancellation system is to obtain a clear signal with higher quality of speech signal. The presence of noise in speech signals can create higher degree of mismatch in performance of speech processing systems used for Speaker Identification as well as Speech Recognition. Inappropriate filtering of noise corresponds to extracting features of noise together with the actual speech signal during the feature extraction process. However, the desired parametric representation carries a high amount of error rate. The presence of broadband noise and a very low SNR deteriorate the intelligibility of most of the recorded speech samples. Speaker's idiosyncratic speech is affected when the noise reduction is carried out. Thus the Speaker Identification

Study on the Selection of Specific Filters for Enhancement of Recorded Speech for Speaker Identification