Abstract

Bursts are produced by closing the oral tract at a place of articulation and suddenly releasing the acoustic energy built-up behind the closure in the tract. The release of energy is an impulselike behavior, and it is followed by a short duration of frication. The burst release is short and mostly weak in nature (compared to sonorant sounds), thus making it difficult to detect its presence in continuous speech. This paper attempts to identify burst onsets based on parameters derived from single frequency filtering (SFF) analysis of speech signals. The SFF envelope and phase information give good spectral and temporal resolutions of certain features of the signal. Signal reconstructed from the SFF phase information is shown to be useful in locating burst onsets. Entropy and spectral distance parameters from the SFF spectral envelopes are used to refine the burst onset candidate set. The identified burst onset locations are compared with manual annotations in the TIMIT database.

Abstract

In this paper, we address the issue of speech polarity detection using strength of impulse-like excitation around epoch. The correct detection of speech polarity is a crucial step for many speech processing algorithms to extract suitable information. Occurrence of errors in the detection of speech polarity could have an impact on the performance of speech systems. Automatic detection of speech polarity has become an important preliminary step for many speech processing algorithms. We propose a method based on the knowledge of impulse-like excitation of speech production mechanism. The impulse-like excitation is reflected across all frequencies including the zero frequency (0 Hz). Using the slope around zero crossings of the zero frequency filtered signal, an automatic speech polarity detection method is proposed. Performance of the proposed method is demonstrated on 8 different speech corpora. The proposed method is compared with the three existing techniques such as gradient of the spurious glottal waveforms (GSGW), oscillating moments-based polarity detection (OMPD) and residual excitation skewness (RESKEW). From the experimental results, it is observed that the performance of the proposed method is comparable or better than the existing methods for the experiments considered.

Abstract

During production of voiced speech, there exists impulse-like excitations due to abrupt closure of vocal folds. These impulselike excitations are often referred as epochs or glottal closure instants (GCIs). The zero frequency filtering (ZFF) method exploits the properties of impulse-like excitation by passing a speech signal through the resonator whose pole pair is located at 0 Hz. As the resonator is unstable, the polynomial growth/decay is observed in the filtered signal, thus requiring a trend removal operation. It is observed that the length of the window for trend removal operation is critical in speech signals where there are more fluctuations in the fundamental frequency (F0). In this paper, a simple finite impulse response (FIR) implementation is proposed. The FIR filter is designed by placing large number of zeros at fs 2 Hz (fs represents the sampling frequency), closer to the unit circle, in the z-plane. Experimental results show that the proposed method is robust and computationally less complex when compared to the ZFF method. Index Terms: Impulse-like excitation, ep

Abstract

In natural conversations, significant part of laughter co-occurs with speech which is referred to as speech-laugh. Hence, speech-laugh will have characteristics of both laughter and neutral speech. But it is not clearly evident how acoustic properties of neutral speech are influenced by its co-occurring laughter. The objective of this study is to analyze the acoustic variations between vowel regions of laughter, speech-laugh and neutral speech. The features based on excitation source characteristics extracted at epochs are considered in this study. Features extracted in the vowel regions of speech-laugh exhibit deviations from that of laughter and neutral speech. These deviations in feature values are exploited to discriminate speech-laugh from laughter and neutral speech. Two different datasets consisting of conversational speech and meeting recordings are used in this analysis. Experimental results show that the discrimination between the three classes obtained by considering vowel regions is better than that of considering the complete utterance.

Abstract

Automatic detection of vowel landmarks is useful in many applications such as automatic speech recognition (ASR), audio search, syllabification of speech and expressive speech processing. In this paper, acoustic features extracted around epochs are proposed for detection of vowel landmarks in continuous speech. These features are based on zero frequency filtering (ZFF) and single frequency filtering (SFF) analyses of speech. Excitation source based features are extracted using ZFF method and vocal tract system based features are extracted using SFF method. Based on these features, a rule-based algorithm is developed for vowel landmark detection (VLD). Performance of the proposed VLD algorithm is studied on three different databases namely, TIMIT (read), NTIMIT (channel degraded) and Switchboard corpus (conversational speech). Results show that the proposed algorithm performs equally well compared to state-of-the-art techniques on TIMIT and betteron NTIMIT and Switchboard corpora. Proposed algorithmalso displays consistent performance on TIMIT and NTIMITdatasets for different levels of noise degradations.

Abstract

In this paper, utilizing the feature of the sonority, the problems of detecting the syllable nuclei and the syllable boundaries are explored. As there is minimal obstruction in the oral cavity in the production of sonorant sounds, sonority feature is used. A method is proposed for extracting the sonority profile from the strength of the dominant resonance frequency using the zero time windowing method. The strength of the dominant resonance frequencies in the specific band of 500 Hz to 1700 Hz is used. The proposed method is evaluated on SVL-DD-NEWScorpora. The results are compared with the Fourier transform method and the energy-based method of extracting the sonority profile. The proposed method performed better compared to other methods, especially in the presence of noise such as white, pink and babble. The method is also evaluated on TIMIT test database and the performance is on par with the current methods in detecting the syllable nuclei.

Abstract

During production of emotional speech there are deviations in the components of speech production mechanism when compared to normal speech. The objective of this study is to capture the deviations in features related to the excitation source component of speech, and to develop a system for automatic recognition of emotions based on these deviations. The emotions considered for this study are: anger, happy, neutral and sad. The study shows that there are useful features in the deviations of the excitation source features at subsegmental level, and they can be exploited to develop an emotion recognition system. A hierarchical binary decision tree approach is used for classification.

Abstract

Sonorant segmentation of speech signals is critical in developing Automatic Speech Recognition (ASR) systems, audio search systems and for automatic segmentation of speech corpora. In this work, acoustic features based on excitation source and vocal tract system characteristics of sonorant sounds are proposed for segmentation of sonorant regions in continuous speech. The features are based on zero frequency resonator signal energy, strength of excitation and dominant resonance frequency around epochs. An algorithm is developed to relate these features in hierarchical manner using knowledge-based approach. The performance of the proposed algorithm is studied on three different datasets, at varying levels of degradation. TIMIT database is used to test the validity and AMI meeting corpus and Telugu (an Indian language) dataset are considered to test the utility of the proposed features.

Abstract

Speech-laugh is a speech-synchronous form of laughter that often occurs in natural conversation. However, there are deviations in features of speech-laugh when compared with laughter and neutral speech individually. The objective of this study is to analyse the excitation source features to capture the deviations between laughter and speech-laughs in voiced regions. The features used in this analysis are based on instantaneous fundamental frequency and strength of excitation (β) at epochs. Modified zero frequency filtering (ZFF) method is used to extract the features. Kullback-Leibler (KL) distances obtained show that there are deviations in excitation source features which can be exploited to develop a method to discriminate speech-laughs from laughter. Experimental results show that features used are robust and speaker independent in discriminating speech-laughs from laughter. Results showing deviations of laughter and speech-laughs from neutral speech were also presented.

Abstract

Studies on the emotion recognition task indicate that there is confusion in discrimination among higher activation states like ‘anger’ and ‘happy’. In this study, features related to excitation source of speech are examined for discriminating ‘anger’ and ‘happy’ emotions. The objective is to explore the features which are independent of lexical content, language, channel and speaker. The features like strength of excitation from zero frequency filtering method and spectral band magnitude energies from short-time spectral analysis are used. Experimental results show that these features can discriminate ‘anger’ and ‘happy’ emotion states to a good extent.