Abstract: Provided are a method for preparing starch using carbon dioxide, a recombinant microorganism, a method for constructing the recombinant microorganism, and a reagent. The method for preparing starch using carbon dioxide comprises: (1) providing energy and carbon sources for microbial cells on the basis of carbon dioxide and extracellular non-optical energy; and (2) generating starch within the microbial cells on the basis of at least one of up-regulated glucose-1-phosphate adenylyltransferase and starch synthase in the microbial cells. In this way, by utilizing non-optical energy, such as electric energy or hydrogen energy, starch can be effectively prepared inside the microbial cells by fixing carbon dioxide.

Abstract: This disclosure provides methods and apparatuses for training an acoustic model which is for implementing cross-speaker style transfer and comprises at least a style encoder. Training data may be obtained, which comprises a text, a speaker ID, a style ID and acoustic features corresponding to a reference audio. A reference embedding vector may be generated, through the style encoder, based on the acoustic features. Adversarial training may be performed to the reference embedding vector with at least the style ID and the speaker ID, to remove speaker information and retain style information. A style embedding vector may be generated, through the style encoder, based at least on the reference embedding vector being performed the adversarial training. Predicted acoustic features may be generated based at least on a state sequence corresponding to the text, a speaker embedding vector corresponding to the speaker ID, and the style embedding vector.

Abstract: Systems are configured for generating spectrogram data characterized by a voice timbre of a target speaker and a prosody style of source speaker by converting a waveform of source speaker data to phonetic posterior gram (PPG) data, extracting additional prosody features from the source speaker data, and generating a spectrogram based on the PPG data and the extracted prosody features. The systems are configured to utilize/train a machine learning model for generating spectrogram data and for training a neural text-to-speech model with the generated spectrogram data.

Abstract: Systems are configured for generating text-to-speech data in a personalized voice by training a neural text-to-speech machine learning model on natural speech data collected from a particular user, validating the identity of the user from which data is collected, and authorizing requests from users to use the personalized voice in generating new speech data. The systems are further configured to train a machine learning model as a neural text-to-speech model with generated personalized speech data.

Abstract: Systems are configured for generating spectrogram data characterized by a voice timbre of a target speaker and a prosody style of source speaker by converting a waveform of source speaker data to phonetic posterior gram (PPG) data, extracting additional prosody features from the source speaker data, and generating a spectrogram based on the PPG data and the extracted prosody features. The systems are configured to utilize/train a machine learning model for generating spectrogram data and for training a neural text-to-speech model with the generated spectrogram data.

Abstract: Systems are configured for generating spectrogram data characterized by a voice timbre of a target speaker and a prosody style of source speaker by converting a waveform of source speaker data to phonetic posterior gram (PPG) data, extracting additional prosody features from the source speaker data, and generating a spectrogram based on the PPG data and the extracted prosody features. The systems are configured to utilize/train a machine learning model for generating spectrogram data and for training a neural text-to-speech model with the generated spectrogram data.

Abstract: Systems are configured for generating spectrogram data characterized by a voice timbre of a target speaker and a prosody style of source speaker by converting a waveform of source speaker data to phonetic posterior gram (PPG) data, extracting additional prosody features from the source speaker data, and generating a spectrogram based on the PPG data and the extracted prosody features. The systems are configured to utilize/train a machine learning model for generating spectrogram data and for training a neural text-to-speech model with the generated spectrogram data.

Abstract: The present disclosure relates to the SALL1 tumor suppressor. Methods of employing SALL1 to treat cancer, as well as the underlying mechanism by which this occurs, also are described.

Abstract: The present disclosure relates to the SALL1 tumor suppressor. Methods of employing SALL1 to treat cancer, as well as the underlying mechanism by which this occurs, also are described.