The purpose of this work
is to investigate a series of biologically inspired modifications to state-of-the-art Mel-
frequency cepstral coefficients (MFCCs) that may improve automatic speech recognition
results. We have provided recommendations to improve speech recognition results de-
pending on signal-to-noise ratio levels of input signals. This work has been motivated by
noise-robust auditory features (NRAF). In the feature extraction technique, after a signal is filtered using bandpass filters, a
spatial derivative step is used to sharpen the results, followed by an envelope detector (recti-
fication and smoothing) and down-sampling for each filter bank before being compressed.
DCT is then applied to the results of all filter banks to produce features. The Hidden-
Markov Model Toolkit (HTK) is used as the recognition back-end to perform speech
recognition given the features we have extracted. In this work, we investigate the
role of filter types, window size, spatial derivative, rectification types, smoothing, down-
sampling and compression and compared the final results to state-of-the-art Mel-frequency
cepstral coefficients (MFCC). A series of conclusions and insights are provided for each
step of the process. The goal of this work has not been to outperform MFCCs; however,
we have shown that by changing the compression type from log compression to 0.07 root
compression we are able to outperform MFCCs for all noisy conditions.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/44801 |
| Date | 12 June 2012 |
| Creators | Javadi, Ailar |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Thesis |
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