Google llc (20240161730). Parallel Tacotron Non-Autoregressive and Controllable TTS simplified abstract

Parallel Tacotron Non-Autoregressive and Controllable TTS

Organization Name

google llc

Inventor(s)

Isaac Elias of Mountain View CA (US)

Jonathan Shen of Mountain View CA (US)

Yu Zhang of Mountain View CA (US)

Ye Jia of Mountain View CA (US)

Ron J. Weiss of New York NY (US)

Yonghui Wu of Fremont CA (US)

Byungha Chun of Tokyo (JP)

Parallel Tacotron Non-Autoregressive and Controllable TTS - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240161730 titled 'Parallel Tacotron Non-Autoregressive and Controllable TTS

Simplified Explanation

The method described in the abstract is for training a non-autoregressive text-to-speech (TTS) model. It involves encoding a reference audio signal and input text sequence, predicting phoneme durations, generating mel-frequency spectrogram sequences, and training the TTS model based on the final spectrogram loss and phoneme duration loss.

• Encoding reference audio signal and input text sequence
• Predicting phoneme durations and determining phoneme duration loss
• Generating mel-frequency spectrogram sequences and determining final spectrogram loss
• Training the TTS model based on final spectrogram loss and phoneme duration loss

Potential Applications

This technology can be applied in various fields such as speech synthesis, virtual assistants, audiobooks, and language learning tools.

Problems Solved

This technology addresses the challenge of generating high-quality speech from text input efficiently and accurately.

Benefits

The benefits of this technology include improved speech synthesis quality, faster generation of speech, and better control over style and prosody in the generated audio.

Potential Commercial Applications

The potential commercial applications of this technology include developing advanced TTS systems for customer service, entertainment, education, and accessibility tools.

Possible Prior Art

One possible prior art for this technology could be the use of variational embeddings in speech synthesis models to disentangle style and prosody information from the reference audio signal.

How does this method compare to autoregressive TTS models in terms of performance and efficiency?

This article does not provide a direct comparison between non-autoregressive TTS models and autoregressive TTS models in terms of performance and efficiency. It would be interesting to see a detailed analysis of the strengths and weaknesses of each approach.

What are the potential limitations or challenges of implementing this method in real-world applications?

The article does not discuss potential limitations or challenges of implementing this method in real-world applications. It would be valuable to explore any constraints or obstacles that may arise when deploying this technology in practical settings.

Original Abstract Submitted

a method for training a non-autoregressive tts model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. the method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. the method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. the method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. the method also includes training the tts model based on the final spectrogram loss and the corresponding phoneme duration loss.