4,325 research outputs found

    Time-Domain Isolated Phoneme Classification Using Reconstructed Phase Spaces

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    This paper introduces a novel time-domain approach to modeling and classifying speech phoneme waveforms. The approach is based on statistical models of reconstructed phase spaces, which offer significant theoretical benefits as representations that are known to be topologically equivalent to the state dynamics of the underlying production system. The lag and dimension parameters of the reconstruction process for speech are examined in detail, comparing common estimation heuristics for these parameters with corresponding maximum likelihood recognition accuracy over the TIMIT data set. Overall accuracies are compared with a Mel-frequency cepstral baseline system across five different phonetic classes within TIMIT, and a composite classifier using both cepstral and phase space features is developed. Results indicate that although the accuracy of the phase space approach by itself is still currently below that of baseline cepstral methods, a combined approach is capable of increasing speaker independent phoneme accuracy

    Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions

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    In this paper, an improved strategy for automated text dependent speaker identification system has been proposed in noisy environment. The identification process incorporates the Neuro-Genetic hybrid algorithm with cepstral based features. To remove the background noise from the source utterance, wiener filter has been used. Different speech pre-processing techniques such as start-end point detection algorithm, pre-emphasis filtering, frame blocking and windowing have been used to process the speech utterances. RCC, MFCC, ?MFCC, ??MFCC, LPC and LPCC have been used to extract the features. After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been used in the learning and identification purposes. Features are extracted by using different techniques to optimize the performance of the identification. According to the VALID speech database, the highest speaker identification rate of 100.000% for studio environment and 82.33% for office environmental conditions have been achieved in the close set text dependent speaker identification system

    Language identification with suprasegmental cues: A study based on speech resynthesis

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    This paper proposes a new experimental paradigm to explore the discriminability of languages, a question which is crucial to the child born in a bilingual environment. This paradigm employs the speech resynthesis technique, enabling the experimenter to preserve or degrade acoustic cues such as phonotactics, syllabic rhythm or intonation from natural utterances. English and Japanese sentences were resynthesized, preserving broad phonotactics, rhythm and intonation (Condition 1), rhythm and intonation (Condition 2), intonation only (Condition 3), or rhythm only (Condition 4). The findings support the notion that syllabic rhythm is a necessary and sufficient cue for French adult subjects to discriminate English from Japanese sentences. The results are consistent with previous research using low-pass filtered speech, as well as with phonological theories predicting rhythmic differences between languages. Thus, the new methodology proposed appears to be well-suited to study language discrimination. Applications for other domains of psycholinguistic research and for automatic language identification are considered

    Statistical Models of Reconstructed Phase Spaces for Signal Classification

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    This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics

    DISSOCIABLE MECHANISMS OF CONCURRENT SPEECH IDENTIFICATION IN NOISE AT CORTICAL AND SUBCORTICAL LEVELS.

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    When two vowels with different fundamental frequencies (F0s) are presented concurrently, listeners often hear two voices producing different vowels on different pitches. Parsing of this simultaneous speech can also be affected by the signal-to-noise ratio (SNR) in the auditory scene. The extraction and interaction of F0 and SNR cues may occur at multiple levels of the auditory system. The major aims of this dissertation are to elucidate the neural mechanisms and time course of concurrent speech perception in clean and in degraded listening conditions and its behavioral correlates. In two complementary experiments, electrical brain activity (EEG) was recorded at cortical (EEG Study #1) and subcortical (FFR Study #2) levels while participants heard double-vowel stimuli whose fundamental frequencies (F0s) differed by zero and four semitones (STs) presented in either clean or noise degraded (+5 dB SNR) conditions. Behaviorally, listeners were more accurate in identifying both vowels for larger F0 separations (i.e., 4ST; with pitch cues), and this F0-benefit was more pronounced at more favorable SNRs. Time-frequency analysis of cortical EEG oscillations (i.e., brain rhythms) revealed a dynamic time course for concurrent speech processing that depended on both extrinsic (SNR) and intrinsic (pitch) acoustic factors. Early high frequency activity reflected pre-perceptual encoding of acoustic features (~200 ms) and the quality (i.e., SNR) of the speech signal (~250-350ms), whereas later-evolving low-frequency rhythms (~400-500ms) reflected post-perceptual, cognitive operations that covaried with listening effort and task demands. Analysis of subcortical responses indicated that while FFRs provided a high-fidelity representation of double vowel stimuli and the spectro-temporal nonlinear properties of the peripheral auditory system. FFR activity largely reflected the neural encoding of stimulus features (exogenous coding) rather than perceptual outcomes, but timbre (F1) could predict the speed in noise conditions. Taken together, results of this dissertation suggest that subcortical auditory processing reflects mostly exogenous (acoustic) feature encoding in stark contrast to cortical activity, which reflects perceptual and cognitive aspects of concurrent speech perception. By studying multiple brain indices underlying an identical task, these studies provide a more comprehensive window into the hierarchy of brain mechanisms and time-course of concurrent speech processing

    Interactive game for the training of portuguese vowels

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    Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

    Asymmetric discrimination of non-speech tonal analogues of vowels

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    Published in final edited form as: J Exp Psychol Hum Percept Perform. 2019 February ; 45(2): 285–300. doi:10.1037/xhp0000603.Directional asymmetries reveal a universal bias in vowel perception favoring extreme vocalic articulations, which lead to acoustic vowel signals with dynamic formant trajectories and well-defined spectral prominences due to the convergence of adjacent formants. The present experiments investigated whether this bias reflects speech-specific processes or general properties of spectral processing in the auditory system. Toward this end, we examined whether analogous asymmetries in perception arise with non-speech tonal analogues that approximate some of the dynamic and static spectral characteristics of naturally-produced /u/ vowels executed with more versus less extreme lip gestures. We found a qualitatively similar but weaker directional effect with two-component tones varying in both the dynamic changes and proximity of their spectral energies. In subsequent experiments, we pinned down the phenomenon using tones that varied in one or both of these two acoustic characteristics. We found comparable asymmetries with tones that differed exclusively in their spectral dynamics, and no asymmetries with tones that differed exclusively in their spectral proximity or both spectral features. We interpret these findings as evidence that dynamic spectral changes are a critical cue for eliciting asymmetries in non-speech tone perception, but that the potential contribution of general auditory processes to asymmetries in vowel perception is limited.Accepted manuscrip

    Speech vocoding for laboratory phonology

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    Using phonological speech vocoding, we propose a platform for exploring relations between phonology and speech processing, and in broader terms, for exploring relations between the abstract and physical structures of a speech signal. Our goal is to make a step towards bridging phonology and speech processing and to contribute to the program of Laboratory Phonology. We show three application examples for laboratory phonology: compositional phonological speech modelling, a comparison of phonological systems and an experimental phonological parametric text-to-speech (TTS) system. The featural representations of the following three phonological systems are considered in this work: (i) Government Phonology (GP), (ii) the Sound Pattern of English (SPE), and (iii) the extended SPE (eSPE). Comparing GP- and eSPE-based vocoded speech, we conclude that the latter achieves slightly better results than the former. However, GP - the most compact phonological speech representation - performs comparably to the systems with a higher number of phonological features. The parametric TTS based on phonological speech representation, and trained from an unlabelled audiobook in an unsupervised manner, achieves intelligibility of 85% of the state-of-the-art parametric speech synthesis. We envision that the presented approach paves the way for researchers in both fields to form meaningful hypotheses that are explicitly testable using the concepts developed and exemplified in this paper. On the one hand, laboratory phonologists might test the applied concepts of their theoretical models, and on the other hand, the speech processing community may utilize the concepts developed for the theoretical phonological models for improvements of the current state-of-the-art applications
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