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Handbook Of Modern Sensors Physics Designs And Applications

An overview of voice conversion systems

Historically, clinical MRI started with main magnetic field strengths in the ∼0.05-0.35T range. In the past 40 years there have been considerable developments in MRI hardware, with one of the primary ones being the trend to higher magnetic fields. While resulting in large improvements in data quality and diagnostic value, such developments have meant that conventional systems at 1.5 and 3T remain relatively expensive pieces of medical imaging equipment, and are out of the financial reach for much of the world. In this review we describe the current state-of-the-art of low-field systems (defined as 0.25-1T), both with respect to its low cost, low foot-print, and subject accessibility. Furthermore, we discuss how low field could potentially benefit from many of the developments that have occurred in higher-field MRI. In the first section, the signal-to-noise ratio (SNR) dependence on the static magnetic field and its impact on the achievable contrast, resolution, and acquisition times are discussed from a theoretical perspective. In the second section, developments in hardware (eg, magnet, gradient, and RF coils) used both in experimental low-field scanners and also those that are currently in the market are reviewed. In the final section the potential roles of new acquisition readouts, motion tracking, and image reconstruction strategies, currently being developed primarily at higher fields, are presented. Level of Evidence: 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmri.26637

Low‐field MRI: An MR physics perspective

Speaker identity is one of the important characteristics of human speech. In voice conversion, we change the speaker identity from one to another, while keeping the linguistic content unchanged. Voice conversion involves multiple speech processing techniques, such as speech analysis, spectral conversion, prosody conversion, speaker characterization, and vocoding. With the recent advances in theory and practice, we are now able to produce human-like voice quality with high speaker similarity. In this article, we provide a comprehensive overview of the state-of-the-art of voice conversion techniques and their performance evaluation methods from the statistical approaches to deep learning, and discuss their promise and limitations. We will also report the recent Voice Conversion Challenges (VCC), the performance of the current state of technology, and provide a summary of the available resources for voice conversion research.

/pdf/an-overview-of-voice-conversion-and-its-challenges-from-3qss1ln75j.pdf

An Overview of Voice Conversion and Its Challenges: From Statistical Modeling to Deep Learning

Diagnosis of stator faults of the single-phase induction motor using acoustic signals

Voice conversion, i.e. modification of a speech signal to sound as if spoken by a different speaker, finds its use in speech synthesis with a new voice without necessity of a new database. This paper introduces two new simple non-linear methods of frequency scale mapping for transformation of voice characteristics between male and female or childish. The frequency scale mapping methods were developed primarily for use in the Czech and Slovak text-to-speech (TTS) system designed for the blind and based on the Pocket PC device platform. It uses cepstral description of the diphone speech inventory of the male speaker using the source-filter speech model or the harmonic speech model. Three new diphone speech inventories corresponding to female, childish and young male voices are created from the original male speech inventory. Listening tests are used for evaluation of voice transformation and quality of synthetic speech.

/pdf/non-linear-frequency-scale-mapping-for-voice-conversion-in-1x67hryms2.pdf

Non-linear frequency scale mapping for voice conversion in text-to-speech system with cepstral description

This article analyzes and compares influence of different types of spectral and prosodic features for Czech and Slovak emotional speech classification based on Gaussian mixture models (GMM). Influence of initial setting of parameters (number of mixture components and used number of iterations) for GMM training process was analyzed, too. Subsequently, analysis was performed to find how correctness of emotion classification depends on the number and the order of the parameters in the input feature vector and on the computation complexity. Another test was carried out to verify the functionality of the proposed two-level architecture comprising the gender recognizer and of the emotional speech classifier. Next tests were realized to find dependence of some negative aspect (processing of the input speech signal with too short time duration, the gender of a speaker incorrectly determined, etc.) on the stability of the results generated during the GMM classification process. Evaluations and tests were realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. In addition, a comparative experiment using the speech data corpus in other language (German) was performed. The mean classification error rate of the whole classifier structure achieves about 21% for all four emotions and both genders, and the best obtained error rate was 3.5% for the sadness style of the female gender. These values are acceptable in this first stage of development of the GMM classifier. On the other hand, the test showed the principal importance of correct classification of the speaker gender in the first level, which has heavy influence on the resulting recognition score of the emotion classification. This GMM classifier should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation.

/pdf/evaluation-of-influence-of-spectral-and-prosodic-features-on-4uoy4inc7a.pdf

Evaluation of influence of spectral and prosodic features on GMM classification of Czech and Slovak emotional speech

Expressive speech synthesis representing different human emotions has been in the interests of researchers for a longer time. Recently, some experiments with storytelling speaking style have been performed. This particular speaking style is suitable for applications aimed at children as well as special applications aimed at blind people. Analyzing human storytellers' speech, we designed a set of prosodic parameters prototypes for converting speech produced by the text-to-speech (TTS) system into storytelling speech. In addition to suprasegmental characteristics (pitch, intensity, and duration) included in these speech prototypes, also information about significant frequencies of spectral envelope and spectral flatness determining degree of voicing was used.

Application of Expressive Speech in TTS System with Cepstral Description

Emotional state of a speaker is accompanied by physiological changes affecting respiration, phonation, and articulation. These changes are manifested mainly in prosodic patterns of F0, energy, and duration, but also in segmental parameters of speech spectrum. Therefore, our new emotional speech synthesis method is supplemented with spectrum modification. It comprises non-linear frequency scale transformation of speech spectral envelope, filtering for emphasizing low or high frequency range, and controlling of spectral noise by spectral flatness measure according to knowledge of psychological and phonetic research. The proposed spectral modification is combined with linear modification of F0 mean, F0 range, energy, and duration. Speech resynthesis with applied modification that should represent joy, anger and sadness is evaluated by a listening test.

Anna Přibilová

Papers

Non-linear frequency scale mapping for voice conversion in text-to-speech system with cepstral description

Evaluation of influence of spectral and prosodic features on GMM classification of Czech and Slovak emotional speech

Application of Expressive Speech in TTS System with Cepstral Description

Spectrum Modification for Emotional Speech Synthesis

Analysis of spectral properties of acoustic noise produced during magnetic resonance imaging