Disentangling the effects of phonation and articulation: Hemispheric asymmetries in the auditory N1m response of the human brain

BACKGROUND: The cortical activity underlying the perception of vowel identity has typically been addressed by manipulating the first and second formant frequency (F1 & F2) of the speech stimuli. These two values, originating from articulation, are already sufficient for the phonetic characterization of vowel category. In the present study, we investigated how the spectral cues caused by articulation are reflected in cortical speech processing when combined with phonation, the other major part of speech production manifested as the fundamental frequency (F0) and its harmonic integer multiples. To study the combined effects of articulation and phonation we presented vowels with either high (/a/) or low (/u/) formant frequencies which were driven by three different types of excitation: a natural periodic pulseform reflecting the vibration of the vocal folds, an aperiodic noise excitation, or a tonal waveform. The auditory N1m response was recorded with whole-head magnetoencephalography (MEG) from ten human subjects in order to resolve whether brain events reflecting articulation and phonation are specific to the left or right hemisphere of the human brain. RESULTS: The N1m responses for the six stimulus types displayed a considerable dynamic range of 115–135 ms, and were elicited faster (~10 ms) by the high-formant /a/ than by the low-formant /u/, indicating an effect of articulation. While excitation type had no effect on the latency of the right-hemispheric N1m, the left-hemispheric N1m elicited by the tonally excited /a/ was some 10 ms earlier than that elicited by the periodic and the aperiodic excitation. The amplitude of the N1m in both hemispheres was systematically stronger to stimulation with natural periodic excitation. Also, stimulus type had a marked (up to 7 mm) effect on the source location of the N1m, with periodic excitation resulting in more anterior sources than aperiodic and tonal excitation. CONCLUSION: The auditory brain areas of the two hemispheres exhibit differential tuning to natural speech signals, observable already in the passive recording condition. The variations in the latency and strength of the auditory N1m response can be traced back to the spectral structure of the stimuli. More specifically, the combined effects of the harmonic comb structure originating from the natural voice excitation caused by the fluctuating vocal folds and the location of the formant frequencies originating from the vocal tract leads to asymmetric behaviour of the left and right hemisphere

Derivation of new and existing discrete-time Kharitonov theorems based on discrete-time reactances

The author first uses a discrete-time reactance approach to give a second proof of existing discrete-time Kharitonov-type results (1979). He then uses the same reactance language to derive a new discrete-time Kharitonov-type theorem which, in some sense, is a very close analog to the continuous-time case. He also points out the relation between discrete-time reactances and the technique of line-spectral pairs (LSP) used in speech compression

Securing Voice-driven Interfaces against Fake (Cloned) Audio Attacks

Voice cloning technologies have found applications in a variety of areas ranging from personalized speech interfaces to advertisement, robotics, and so on. Existing voice cloning systems are capable of learning speaker characteristics and use trained models to synthesize a person's voice from only a few audio samples. Advances in cloned speech generation technologies are capable of generating perceptually indistinguishable speech from a bona-fide speech. These advances pose new security and privacy threats to voice-driven interfaces and speech-based access control systems. The state-of-the-art speech synthesis technologies use trained or tuned generative models for cloned speech generation. Trained generative models rely on linear operations, learned weights, and excitation source for cloned speech synthesis. These systems leave characteristic artifacts in the synthesized speech. Higher-order spectral analysis is used to capture differentiating attributes between bona-fide and cloned audios. Specifically, quadrature phase coupling (QPC) in the estimated bicoherence, Gaussianity test statistics, and linearity test statistics are used to capture generative model artifacts. Performance of the proposed method is evaluated on cloned audios generated using speaker adaptation- and speaker encoding-based approaches. Experimental results for a dataset consisting of 126 cloned speech and 8 bona-fide speech samples indicate that the proposed method is capable of detecting bona-fide and cloned audios with close to a perfect detection rate.Comment: 6 pages, The 2nd IEEE International Workshop on "Fake MultiMedia" (FakeMM'19) March 28-30, 2019, San Jose, CA, US

Comparison of input devices in an ISEE direct timbre manipulation task

The representation and manipulation of sound within multimedia systems is an important and currently under-researched area. The paper gives an overview of the authors' work on the direct manipulation of audio information, and describes a solution based upon the navigation of four-dimensional scaled timbre spaces. Three hardware input devices were experimentally evaluated for use in a timbre space navigation task: the Apple Standard Mouse, Gravis Advanced Mousestick II joystick (absolute and relative) and the Nintendo Power Glove. Results show that the usability of these devices significantly affected the efficacy of the system, and that conventional low-cost, low-dimensional devices provided better performance than the low-cost, multidimensional dataglove

Modeling the Liquid, Nasal, AND Vowel Transitions OF North American English Using Linear Predictive Filters and Line Spectral Frequency Interpolations for Use in a Speech Synthesis System

A speech synthesis system with an original user interface is being developed. In contrast to most modern synthesizers, this system is not text to speech (TTS). This system allows the user to control vowels, vowel transitions, and consonant sounds through a simple 2-d vowel pad and consonant buttons. In this system, a synthesized glottal waveform is passed through vowel filters to create vowel sounds. Several filters were calculated from recordings of vowels using linear predictive coding (LPC). The rest of the vowels in the North American English vowel space were found using interpolation techniques with line spectral frequencies (LSF). The effectiveness and naturalness of the speech created from transitions between these filters was tested. In addition to the vowel filters, filters for nasal and liquid consonants were found using LPC analysis. Transition filters between these consonants and vowels were determined using LSFs. These transitions were tested as well

Communications Biophysics

Contains reports on seven research projects split into three sections.National Institutes of Health (Grant 5 PO1 NS13126)National Institutes of Health (Grant 1 RO1 NS18682)National Institutes of Health (Training Grant 5 T32 NS07047)National Science Foundation (Grant BNS77-16861)National Institutes of Health (Grant 1 F33 NS07202-01)National Institutes of Health (Grant 5 RO1 NS10916)National Institutes of Health (Grant 5 RO1 NS12846)National Institutes of Health (Grant 1 RO1 NS16917)National Institutes of Health (Grant 1 RO1 NS14092-05)National Science Foundation (Grant BNS 77 21751)National Institutes of Health (Grant 5 R01 NS11080)National Institutes of Health (Grant GM-21189

Mixed Distance Measures for Optimizing Concatenative Vocabularies for Speech Synthesis: A Thesis Proposal

Synthesized speech from text-to-speech systems is generally produced from the concatenation of small units of speech. The concatenation process can be complex, involving smoothing and context dependent adjustments to the speech. The overall quality of the speech produced will depend in large part on the quality of the elements used for concatenation. Selection and evaluation of these elements has been done entirely by hand. The proposed work addresses the process by which these concatenative elements are created from a natural voice and optimized. The optimization uses distance measures which exploit detailed information on the structure of the speech signals

Using a low-bit rate speech enhancement variable post-filter as a speech recognition system pre-filter to improve robustness to GSM speech

Includes bibliographical references.Performance of speech recognition systems degrades when they are used to recognize speech that has been transmitted through GS1 (Global System for Mobile Communications) voice communication channels (GSM speech). This degradation is mainly due to GSM speech coding and GSM channel noise on speech signals transmitted through the network. This poor recognition of GSM channel speech limits the use of speech recognition applications over GSM networks. If speech recognition technology is to be used unlimitedly over GSM networks recognition accuracy of GSM channel speech has to be improved. Different channel normalization techniques have been developed in an attempt to improve recognition accuracy of voice channel modified speech in general (not specifically for GSM channel speech). These techniques can be classified into three broad categories, namely, model modification, signal pre-processing and feature processing techniques. In this work, as a contribution toward improving the robustness of speech recognition systems to GSM speech, the use of a low-bit speech enhancement post-filter as a speech recognition system pre-filter is proposed. This filter is to be used in recognition systems in combination with channel normalization techniques

Better Safe Than Sorry: An Adversarial Approach to Improve Social Bot Detection

The arm race between spambots and spambot-detectors is made of several cycles (or generations): a new wave of spambots is created (and new spam is spread), new spambot filters are derived and old spambots mutate (or evolve) to new species. Recently, with the diffusion of the adversarial learning approach, a new practice is emerging: to manipulate on purpose target samples in order to make stronger detection models. Here, we manipulate generations of Twitter social bots, to obtain - and study - their possible future evolutions, with the aim of eventually deriving more effective detection techniques. In detail, we propose and experiment with a novel genetic algorithm for the synthesis of online accounts. The algorithm allows to create synthetic evolved versions of current state-of-the-art social bots. Results demonstrate that synthetic bots really escape current detection techniques. However, they give all the needed elements to improve such techniques, making possible a proactive approach for the design of social bot detection systems.Comment: This is the pre-final version of a paper accepted @ 11th ACM Conference on Web Science, June 30-July 3, 2019, Boston, U