Abstract: Example methods and systems for enhanced accent conversion are provided. In an example method, an audio input is accessed and a first set of features is extracted from the audio input using a first neural network. A second set of features is further determined based on the first set of features using a second neural network. The second neural network is configured to extract features that are accent-neutral. The second set of features is then transformed into output audio data using a decode based on a designated accent identifier. The example method provides an enhanced accent conversion for video conferencing and audio recording.

Abstract: Techniques for cross-lingual voice cloning for low-resource languages are provided. In an example method, a computing system receives a first speech sample, the first speech sample characterized by a first voice and spoken in a first language. The computing system receives a first text input in a second language. The computing system generates, by a first ML model, a first encoded representation of the first text input. The computing system generates, by a second ML model, a first spectrogram of the first text input spoken in the first voice in the second language, in which the generating is based on an embedded representation of the first speech sample, in which the first ML model is different from the second ML model. The computing system generates an audio output based on the first spectrogram.

Abstract: Systems and methods for machine learning based audio watermarking for videoconferencing are provided. For example, a computing device accesses an original audio signal and a watermark to be embedded into the original audio signal and extracts, using an audio encoder, a set of audio features from the original audio signal. The audio encoder is a machine learning model. The computing device further extracts, using a watermark encoder, a set of watermark features from the watermark. The watermark encoder is also a machine learning model. The computing device combines the set of audio features and the set of watermark features to generate a set of features, compresses the set of features, and transmits the compressed set of features.

Abstract: Techniques for cross-lingual voice cloning for low-resource languages are provided. In an example method, a computing device receives a first speech sample, the first speech sample characterized by a first voice and spoken in a first language and a text input in a second language. The computing device generates, using a first trained ML model trained to encode the text input to an encoded representation that characterizes the text input spoken in a second voice in the second language, the encoded representation of the text input. The computing device generates, using a second trained ML model trained to generate a spectrogram based on the encoded representation of the text input spoken in the second language, the spectrogram of the text input, the spectrogram characterized by the text input spoken in the first voice in the second language. The computing device generates an audio output based on the spectrogram.

Abstract: Systems and methods of multi-modal adversarial training for zero-shot voice cloning are provided. A communication platform provides training transcript data and training speaker data to a text-to-speech (TTS) model to obtains synthesized audio data comprising synthesized acoustic features and synthesized prosodic features. The communication platform determines determine a first classification prediction using a first discriminator model and a second classification prediction using a second discriminator model. The communication platform trains the first discriminator model and the second discriminator model based on the first classification prediction and the second classification prediction. The communication platform trains the TTS model to obtain a trained TTS model based on ground truth acoustic features, ground truth prosodic features, the synthesized acoustic features, the synthesized prosodic features, the first classification prediction, and the second classification prediction.

Abstract: The accuracy of automatic speech recognition (ASR) tasks is improved using trained models. A speech recognition model is applied in a noisy environment where speech is spoken at a distance from the microphones. The techniques may include extracting speech features, data augmentation by adding feature perturbation, and/or a multi-domain end-to-end speech recognition model. In some implementations, the described technology includes using a teacher-group knowledge distillation strategy to train a deep end-to-end speech recognition model on original speech samples and the sample speech augmentation of the original speech samples, that outputs recognized text transcriptions corresponding to speech detected in the original speech samples and the sample speech augmentation.

Abstract: The accuracy of automatic speech recognition (ASR) tasks is improved using trained models. A speech recognition model is applied in a noisy environment where speech is spoken at a distance from the microphones. The techniques may include extracting speech features, data augmentation by adding feature perturbation, and/or a multi-domain end-to-end speech recognition model. In some implementations, the described technology includes using a teacher-group knowledge distillation strategy to train a deep end-to-end speech recognition model on original speech samples and the sample speech augmentation of the original speech samples, that outputs recognized text transcriptions corresponding to speech detected in the original speech samples and the sample speech augmentation.

Abstract: The accuracy of automatic speech recognition (ASR) tasks is improved using trained models. A speech recognition model is applied in a noisy environment where speech is spoken at a distance from the microphones. The techniques may include extracting speech features, data augmentation by adding feature perturbation, and/or a multi-domain end-to-end speech recognition model. In some implementations, the described technology includes using a teacher-group knowledge distillation strategy to train a deep end-to-end speech recognition model on original speech samples and the sample speech augmentation of the original speech samples, that outputs recognized text transcriptions corresponding to speech detected in the original speech samples and the sample speech augmentation.