Fine structure spectrography, with applications in human and machine speech processing.
Published: 2004
Language: eng
Number of pages: 170
Description: An audition-inspired algorithm for time-varying spectral analysis is proposed. It consists of many highly overlapping wideband Filter/Detectors, followed by a peak detector. Analysis with synthetic modulated signals illustrates how the proposed method demodulates signals. The resulting display, referred to as a 'Fine Structure Spectrogram' or FSS, shows the fine structure of modulations in substantially higher detail than in conventional spectrograms. In the Fourier domain, a less generalized form of the FSS can be implemented using ridge extraction on a highly 'oversampled' STFT. Evaluation of estimation errors with test signals indicates that for single component signals, better estimation requires relatively wide filters, small filter separations, and small probing intervals. In contrast, with multi-component signals, larger filter bandwidths increase the estimation error if two components are close in frequency.In speech, the FSS can detect small frequency and amplitude modulations in the formants of speech. It also identifies significant 'extra' time-frequency components not detected by other methods. In the case of one extra component, detailed analysis confirms that it is not an artifact. Three applications of the FSS are investigated. First, the FSS is compared to standard Mel Frequency Cepstral Coefficients (MFCC) as a front end in an automatic speaker identification system. When the MFCC is augmented with FSS-based features, there is a small (0.86%) but statistically significant improvement ( P &lt; 0.05) in performance over the MFCC alone, if data from the optimization and testing stages are combined. In the second application, the FSS is used to detect a speech pitch-evoked response in the EEG, when subjects are presented with a vowel and a version of the vowel with a 'missing fundamental'. In both cases, and with all subjects, the FSS detects a pitch track in the EEG that very accurately follows the acoustic pitch track (normalized mean absolute error &lt;0.57% for all subjects). In the third application, an FSS-based method is proposed as a Pitch Detection Algorithm (PDA). With test cases of difficult-to-handle anomalous voice types, and compared to other PDA's, the FSS-based pitch track is characterized by smoothness, rarity of catastrophic failure, and graceful recovery from failure.
Notes: Advisers:  Hans Kunov; Willy Wong.

Thesis (Ph.D.)--University of Toronto, 2004.

Electronic version licensed for access by U. of T. users.

Source: Dissertation Abstracts International, Volume: 65-05, Section: B, page: 2538.