9,738 research outputs found
Design of a Speech Corpus for Research on Cross-Lingual Prosody Transfer
Since the prosody of a spoken utterance carries information about its discourse function, salience, and speaker attitude, prosody mod- els and prosody generation modules have played a crucial part in text-to- speech (TTS) synthesis systems from the beginning, especially those set not only on sounding natural, but also on showing emotion or particular speaker intention. Prosody transfer within speech-to-speech translation is a recent research area with increasing importance, with one of its most important research topics being the detection and treatment of salient events, i.e. instances of prominence or focus which do not result from syn- tactic constraints, but are rather products of semantic or pragmatic level eects. This paper presents the design and the guidelines for the creation of a multilingual speech corpus containing prosodically rich sentences, ultimately aimed at training statistical prosody models for multilingual prosody transfer in the context of expressive speech synthesis
Generating expressive speech for storytelling applications
Work on expressive speech synthesis has long focused on the expression of basic emotions. In recent years, however, interest in other expressive styles has been increasing. The research presented in this paper aims at the generation of a storytelling speaking style, which is suitable for storytelling applications and more in general, for applications aimed at children. Based on an analysis of human storytellers' speech, we designed and implemented a set of prosodic rules for converting "neutral" speech, as produced by a text-to-speech system, into storytelling speech. An evaluation of our storytelling speech generation system showed encouraging results
Self-imitating Feedback Generation Using GAN for Computer-Assisted Pronunciation Training
Self-imitating feedback is an effective and learner-friendly method for
non-native learners in Computer-Assisted Pronunciation Training. Acoustic
characteristics in native utterances are extracted and transplanted onto
learner's own speech input, and given back to the learner as a corrective
feedback. Previous works focused on speech conversion using prosodic
transplantation techniques based on PSOLA algorithm. Motivated by the visual
differences found in spectrograms of native and non-native speeches, we
investigated applying GAN to generate self-imitating feedback by utilizing
generator's ability through adversarial training. Because this mapping is
highly under-constrained, we also adopt cycle consistency loss to encourage the
output to preserve the global structure, which is shared by native and
non-native utterances. Trained on 97,200 spectrogram images of short utterances
produced by native and non-native speakers of Korean, the generator is able to
successfully transform the non-native spectrogram input to a spectrogram with
properties of self-imitating feedback. Furthermore, the transformed spectrogram
shows segmental corrections that cannot be obtained by prosodic
transplantation. Perceptual test comparing the self-imitating and correcting
abilities of our method with the baseline PSOLA method shows that the
generative approach with cycle consistency loss is promising
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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