21 research outputs found
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
This paper proposes Virtuoso, a massively multilingual speech-text joint
semi-supervised learning framework for text-to-speech synthesis (TTS) models.
Existing multilingual TTS typically supports tens of languages, which are a
small fraction of the thousands of languages in the world. One difficulty to
scale multilingual TTS to hundreds of languages is collecting high-quality
speech-text paired data in low-resource languages. This study extends Maestro,
a speech-text joint pretraining framework for automatic speech recognition
(ASR), to speech generation tasks. To train a TTS model from various types of
speech and text data, different training schemes are designed to handle
supervised (paired TTS and ASR data) and unsupervised (untranscribed speech and
unspoken text) datasets. Experimental evaluation shows that 1) multilingual TTS
models trained on Virtuoso can achieve significantly better naturalness and
intelligibility than baseline ones in seen languages, and 2) they can
synthesize reasonably intelligible and naturally sounding speech for unseen
languages where no high-quality paired TTS data is available.Comment: Submitted to ICASSP 202
LibriTTS-R: A Restored Multi-Speaker Text-to-Speech Corpus
This paper introduces a new speech dataset called ``LibriTTS-R'' designed for
text-to-speech (TTS) use. It is derived by applying speech restoration to the
LibriTTS corpus, which consists of 585 hours of speech data at 24 kHz sampling
rate from 2,456 speakers and the corresponding texts. The constituent samples
of LibriTTS-R are identical to those of LibriTTS, with only the sound quality
improved. Experimental results show that the LibriTTS-R ground-truth samples
showed significantly improved sound quality compared to those in LibriTTS. In
addition, neural end-to-end TTS trained with LibriTTS-R achieved speech
naturalness on par with that of the ground-truth samples. The corpus is freely
available for download from \url{http://www.openslr.org/141/}.Comment: Accepted to Interspeech 202
Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Speech restoration (SR) is a task of converting degraded speech signals into
high-quality ones. In this study, we propose a robust SR model called Miipher,
and apply Miipher to a new SR application: increasing the amount of
high-quality training data for speech generation by converting speech samples
collected from the Web to studio-quality. To make our SR model robust against
various degradation, we use (i) a speech representation extracted from w2v-BERT
for the input feature, and (ii) a text representation extracted from
transcripts via PnG-BERT as a linguistic conditioning feature. Experiments show
that Miipher (i) is robust against various audio degradation and (ii) enable us
to train a high-quality text-to-speech (TTS) model from restored speech samples
collected from the Web. Audio samples are available at our demo page:
google.github.io/df-conformer/miipher/Comment: Accepted to WASPAA 202
RAPID PROGRESSION OF ACUTE CHOLANGITIS TO SECONDARY SCLEROSING CHOLANGITIS-A CASE REPORT-
Superconducting Time-of-flight Mass Spectrometry Systems for Biomolecules using Superconducting Digital Circuits
Synthesis of MoS2 Thin Film by Chemical Vapor Deposition Method and Discharge Characteristics as a Cathode of the Lithium Secondary Battery
Engineering thermal conductance using a two-dimensional phononic crystal
Abstract. Controlling thermal transport has become relevant in recent years. Traditionally, this control has been achieved by tuning the scattering of phonons by including various types of scattering centres in the material (nanoparticles, impurities, etc). Here we take another approach and demonstrate that one can also use coherent band structure effects to control phonon thermal conductance, with the help of periodically nanostructured phononic crystals. We perform the experiments at low temperatures below 1 K, which not only leads to negligible bulk phonon scattering, but also increases the wavelength of the dominant thermal phonons by more than two orders of magnitude compared to room temperature. Thus, phononic crystals with lattice constants ≥1 μm are shown to strongly reduce the thermal conduction. The observed effect is in quantitative agreement with the theoretical calculation presented, which accurately determined the ballistic thermal conductance in a phononic crystal device.peerReviewe
Operation of superconducting nano-stripline detector (SSLD) mounted on cryogen-free cryostat
Recently, various types of superconducting detectors have been applied to time-of-flight mass spectrometers (TOF MS) because they can achieve 100% detection efficiency for a wide mass range from atoms to huge biomolecules. The wide mass range coverage is impossible with conventional microchannel plate (MCP) ion detectors. Superconducting stripline detectors (SSLD) that consist of several hundreds of superconducting nanostrips with a width of < 1 μm and a thickness of a few tens nm have a high sensitivity for biomolecules and a response time of ∼ 1 ns that cannot be achieved by other superconducting detectors. For the practical use of SSLD, an easy operation system is necessary. In this study, we will present the proper operation of SSLD which is mounted on a cryogen-free pulse tube cryostat