Abstract

In production of voiced speech, epochs or glottal closure instants (GCIs) refer to the instants of significant excitation of the vocal tract. Extraction of GCIs is used as a pre-processing stage in many areas of speech technology, such as in prosody modification, speech syn- thesis and voice source analysis. In the past decades, several GCI detection algorithms have been developed and most of them provide excellent results for speech signals produced using modal (normal) type of phonation. There are, however, no studies comparing multi- ple state-of-the-art GCI detection methods in emotional speech. In this paper, we compare six GCI detection algorithms using emo- tional speech and known evaluation metrics. We use the Berlin EMO-DB acted emotional speech database which contains seven emotions and simultaneous electroglottography (EGG) recordings as ground truth. The results show that all six GCI detection algo- rithms give best performance in processing speech of neutral emo- tion and that the performance degrade particularly in emotions of high arousal (anger and joy). To improve the performance of GCI detection in emotional speech, the study underlines the importance of local average pitch period estimates.

Abstract

n generation of emotional speech, there are deviations in the speech production features when compared to neutral (non-emotional) speech. The objective of this study is to capture the deviations in features related to the excitation component of speech and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered in this study are anger, happiness, sadness and neutral state. The study shows that there are useful features in the deviations of the excitation features, which can be exploited to develop an emotion recognition system. The excitation features used in this study are the instantaneous fundamental frequency (), the strength of excitation, the energy of excitation and the ratio of the high-frequency to low-frequency band energy (). A hierarchical binary decision tree approach is used to develop an emotion recognition system with neutral speech …

Abstract

A method is proposed for time delay estimation (TDE) from mixed source (speaker) signals collected at two spatially separated microphones. The key idea in this proposal is that the crosscorrelation between corresponding segments of the mixed source signals is computed using the outputs of single frequency filtering (SFF) obtained at several frequencies, rather than using the collected waveforms directly. The advantage of the SFF output is that it will have high signal-to-noise ratio regions in both time and frequency domains. Also it gives multiple evidences, one from each of the SFF outputs. These multiple evidences are combined to obtain robustness in the TDE. The estimated time delays can be used to determine the number of speakers present in the mixed signals. The TDE is shown to be robust against different types and levels of degradations. The results are shown for actual mixed signals collected at two spatially separated microphones in a live laboratory environment, where the mixed signals contain speech from several spatially distributed speakers.

Abstract

Fricatives are produced by creating a turbulence in the air– flow by passing it through a stricture in the vocal tract cavity. Fricatives are characterized by their noise–like behavior, which makes it difficult to analyze. Difference in the place of articulation leads to different classes of fricatives. Identification of fricative segment boundaries in speech helps in improving the performance of several applications. The present study attempts towards the identification and classification of fricative segments in continuous speech, based on the statistical behavior of instantaneous spectral characteristics. The proposed method uses parameters such as the dominant resonance frequencies, the center of gravity along with the statistical moments of the spectrum obtained using the zero time windowing (ZTW) method. The ZTW spectra exhibits a high temporal resolution and therefore gives accurate segment boundaries in speech. The proposed algorithm is tested on the TIMIT dataset for English language. A high identification rate of 97.5% is achieved for segment boundaries of the sibilant fricative class. Voiced nonsibilants show a lower identification rate than their voiceless counterparts due to their vowel–like spectral characteristics. A high classification rate of 93.2% is achieved be

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.

Abstract

In this study, classification of the phonation modes in singing voice is carried out. Phonation modes in singing voice can be described using four categories: breathy, neutral, flow and pressed phonations. Previous studies on the classification of phonation modes use voice quality features derived from inverse filtering which lack in accuracy. This is due to difficulty in deriving the excitation source features using inverse filtering from singing voice. We propose to use the excitation source features that are derived directly from the signal. It is known that, the characteristics of the excitation source vary in different phonation types due to the vibration of the vocal folds together with the respiratory effort (lungs effort). In the present study, we are exploring excitation source features derived from the modified zero frequency filtering (ZFF) method. Apart from excitation source features, we also explore cepstral coefficients derived from single frequency filtering (SFF) method for the analysis and classification of phonation types in singing voice. Index Terms: Singing voice, Excitation source, Ph

Abstract

In this paper, we consider breathy to tense voices, which are often considered to be opposite ends of a voice quality continuum. Along with these, other aspects of a speaker's voice play an important role to convey the information to the listener such as mood, attitude and emotional state. The glottal pulse characteristics in different phonation types vary due to the tension of laryngeal muscles together with the respiratory effort. In the present study, we are deriving the features that can capture effects of excitation on the vocal tract system through a signal processing method, called as zero-time windowing (ZTW) method. The ZTW method gives the instantaneous spectrum which captures the changes in the speech production mechanism, providing higher spectral resolution. The cepstral coefficients derived from ZTW method are used for the classification of phonation types. Along with zero-time windowing cepstral coefficients (ZTWCCs), we use the excitation source features derived from zero frequency filtering (ZFF) method. The excitation features used are: strength of excitation, energy of excitation, loudness measure and ZFF signal energy. Classification experiments using ZTWCC and excitation features reveal a significant improvement in the detection of phonation type compared to the existing voice quality features and MFCC features

Abstract

This paper focuses on the problem of estimating fundamental frequency from singing voice. Estimation of fundamental frequency is a well studied topic in the speech research community. From the recent studies on fundamental frequency estimation from singing voice with state-of-art methods proposed for speech, there exists a significant gap in accuracy for singing voice. This is mainly because of the wider and rapid variations in pitch in singing voice compared to that in speech. To overcome this, in this paper we propose a method to derive the fundamental frequency from singing voice by exploiting the harmonics of impulse-like excitation in sequence of glottal cycles. The proposed method is compared with the eight state-of-art methods such as YIN, SWIPE, YAAPT, RAPT, SRH, SFF CEP, PEFAC and SHRP on the LYRICS singing database. From the experimental results, it is observed that the accuracy of fundamental frequency by the proposed method is better than many state-of-art methods in various singing categories and laryngeal mechanisms

Abstract

High arousal speech is produced by speakers when they raise their loudness levels. There are deviations from neutral speech, especially in the excitation component of the speech production mechanism in the high arousal mode. In this study, a parameter, called the time-frequency spectral error (TF e) is derived using the single frequency filtering (SFF) spectrogram. It is used to characterize the high arousal regions in speech signals. The proposed parameter captures the fine temporal and spectral variations due to changes in the excitation source.

Abstract

Nasals and approximants consonants are often confused with each other. Despite the distinction in the production mechanism, these two sound classes exhibit a similar low frequency behavior, and lack significant high frequency content. The present study uses a spectral representation obtained using the zero time windowing (ZTW) analysis of speech, for the task of distinction between these two. The instantaneous spectral representation has good resolution at resonances, which helps to highlight the difference in the acoustic vocal tract system response for these sounds. The ZTW spectra around the regions of glottal closure instants are averaged to derive parameters for their classification in continuous speech. A set of parameters based on the dominant resonances, center of gravity, band energy ratio, and cumulative spectral sum in low frequencies, is derived from the average spectrum. The paper proposes classification using a knowledge–based approach and training a support vector machine. These classifiers are tested on utterances from different English speakers in the TIMIT dataset. The proposed methods result in an average classification accuracy of 90% between the two classes in continuous speech.