10,387 research outputs found
Analysis of a Modern Voice Morphing Approach using Gaussian Mixture Models for Laryngectomees
This paper proposes a voice morphing system for people suffering from
Laryngectomy, which is the surgical removal of all or part of the larynx or the
voice box, particularly performed in cases of laryngeal cancer. A primitive
method of achieving voice morphing is by extracting the source's vocal
coefficients and then converting them into the target speaker's vocal
parameters. In this paper, we deploy Gaussian Mixture Models (GMM) for mapping
the coefficients from source to destination. However, the use of the
traditional/conventional GMM-based mapping approach results in the problem of
over-smoothening of the converted voice. Thus, we hereby propose a unique
method to perform efficient voice morphing and conversion based on GMM,which
overcomes the traditional-method effects of over-smoothening. It uses a
technique of glottal waveform separation and prediction of excitations and
hence the result shows that not only over-smoothening is eliminated but also
the transformed vocal tract parameters match with the target. Moreover, the
synthesized speech thus obtained is found to be of a sufficiently high quality.
Thus, voice morphing based on a unique GMM approach has been proposed and also
critically evaluated based on various subjective and objective evaluation
parameters. Further, an application of voice morphing for Laryngectomees which
deploys this unique approach has been recommended by this paper.Comment: 6 pages, 4 figures, 4 tables; International Journal of Computer
Applications Volume 49, Number 21, July 201
A silent speech system based on permanent magnet articulography and direct synthesis
In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies
Whisper-to-speech conversion using restricted Boltzmann machine arrays
Whispers are a natural vocal communication mechanism, in which vocal cords do not vibrate normally. Lack of glottal-induced pitch leads to low energy, and an inherent noise-like spectral distribution reduces intelligibility. Much research has been devoted to processing of whispers, including conversion of whispers to speech. Unfortunately, among several approaches, the best reconstructed speech to date still contains obviously artificial muffles and suffers from an unnatural prosody. To address these issues, the novel use of multiple restricted Boltzmann machines (RBMs) is reported as a statistical conversion model between whisper and speech spectral envelopes. Moreover, the accuracy of estimated pitch is improved using machine learning techniques for pitch estimation within only voiced (V) regions. Both objective and subjective evaluations show that this new method improves the quality of whisper-reconstructed speech compared with the state-of-the-art approaches
Sampling-based speech parameter generation using moment-matching networks
This paper presents sampling-based speech parameter generation using
moment-matching networks for Deep Neural Network (DNN)-based speech synthesis.
Although people never produce exactly the same speech even if we try to express
the same linguistic and para-linguistic information, typical statistical speech
synthesis produces completely the same speech, i.e., there is no
inter-utterance variation in synthetic speech. To give synthetic speech natural
inter-utterance variation, this paper builds DNN acoustic models that make it
possible to randomly sample speech parameters. The DNNs are trained so that
they make the moments of generated speech parameters close to those of natural
speech parameters. Since the variation of speech parameters is compressed into
a low-dimensional simple prior noise vector, our algorithm has lower
computation cost than direct sampling of speech parameters. As the first step
towards generating synthetic speech that has natural inter-utterance variation,
this paper investigates whether or not the proposed sampling-based generation
deteriorates synthetic speech quality. In evaluation, we compare speech quality
of conventional maximum likelihood-based generation and proposed sampling-based
generation. The result demonstrates the proposed generation causes no
degradation in speech quality.Comment: Submitted to INTERSPEECH 201
Nonparallel Emotional Speech Conversion
We propose a nonparallel data-driven emotional speech conversion method. It
enables the transfer of emotion-related characteristics of a speech signal
while preserving the speaker's identity and linguistic content. Most existing
approaches require parallel data and time alignment, which is not available in
most real applications. We achieve nonparallel training based on an
unsupervised style transfer technique, which learns a translation model between
two distributions instead of a deterministic one-to-one mapping between paired
examples. The conversion model consists of an encoder and a decoder for each
emotion domain. We assume that the speech signal can be decomposed into an
emotion-invariant content code and an emotion-related style code in latent
space. Emotion conversion is performed by extracting and recombining the
content code of the source speech and the style code of the target emotion. We
tested our method on a nonparallel corpora with four emotions. Both subjective
and objective evaluations show the effectiveness of our approach.Comment: Published in INTERSPEECH 2019, 5 pages, 6 figures. Simulation
available at http://www.jian-gao.org/emoga
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