Detection of Glottal Closure Instants in Degraded Speech Using Single Frequency Filtering Analysis

Abstract

Impulse-like characteristics of excitation occur at the glottal closure instant (GCI) due to sharp closure of the vibrating vocal folds in each glottal cycle. The GCIs are detected from the excitation component of the speech signal, and the excitation component is derived using inverse filtering or its variants. In this paper we propose a method for GCI detection based on single frequency filtering (SFF) of the speech signal. The SFF output has high signal-to-noise ratio (SNR) property in speech regions. The variance (across frequency) contour computed from the SFF output show rapid changes around the GCIs, and these rapid changes can be observed even when the speech signal is degraded. Thus the GCI locations can be extracted even from degraded speech using the SFF analysis. The robustness of the method is demonstrated for several cases of degradation of speech signal.

Mel-Weighted Single Frequency Filtering Spectrogram for Dialect Identification

“…In addition, we conclude that the proposed Mel-SFF spectrogram system distinguishes dialects better from short utterances than its STFT-based reference system. In the future, we plan to explore the Mel-SFF spectrogram derived features for dialect identification in noisy conditions [27], [28], [30] and for larger corpora. Further, we plan to investigate the complementary information between the SFF based spectrograms and zero-time windowing (ZTW) based spectrograms, which were shown to give better performance over STFT [42]- [44].…”

“…The SFF method is used to derive the amplitude envelope of the speech signal at every sample for a given frequency [32]. The SFF spectrum has been shown to be useful in finding burst-onset points [29] and glottal closure instants [30], and it has been demonstrated to exhibit high spectral resolution for important speech features such as harmonics and resonances [27].…”

“…The spectrum computed by single frequency filtering (SFF) has been shown to give good spectral resolution to indicate harmonics and resonances [27] and good temporal resolution to model speech excitation features such as impulse-like events [28]. The SFF spectrum has also shown promising performance in determining burstonset points related to voice-onset time (VOT) and glottal closure instances compared to the short-time Fourier transform (STFT) spectrum [28]- [30]. Previous studies in dialect identification have shown the significance of VOT for identification of accent [31].…”

Time Delay Estimation from Mixed Multispeaker Speech Signals Using Single Frequency Filtering

“…The effects due to waveform distortion are reduced in the SFF outputs, as we consider the envelope of the signal at each frequency separately. Although filtering causes smearing of the signal in the time domain due to closeness of the root to the unit circle in the z-plane, the impulse sequence characteristics are preserved in the SFF output at each frequency [2,9]. Thus, the crosscorrelation of the SFF envelopes is not affected by the waveform distortion.…”

A Quantitative Comparison of Epoch Extraction Algorithms for Telephone Speech

“…From the studies in [18], it was observed that most of the epoch detection methods were shown to provide good accuracy on the speech data collected in the lab environments. Also, some attempts were made to see the effectiveness of these methods for additive noise degraded conditions [19][20][21][22][23]. However, there are not many attempts in GCI detection for the degraded conditions like telephone quality speech.…”