View source for 18654278. USING SPEECH RECOGNITION TO IMPROVE CROSS-LANGUAGE SPEECH SYNTHESIS simplified abstract (Google LLC)

=USING SPEECH RECOGNITION TO IMPROVE CROSS-LANGUAGE SPEECH SYNTHESIS=


==Organization Name==

[[:Category:Google LLC|Google LLC]]


[[Category:Google LLC]]

==Inventor(s)==

[[:Category:Zhehuai Chen of Edgewater NJ (US)|Zhehuai Chen of Edgewater NJ (US)]][[Category:Zhehuai Chen of Edgewater NJ (US)]]

[[:Category:Bhuvana Ramabhadran of Mt. Kisco NY (US)|Bhuvana Ramabhadran of Mt. Kisco NY (US)]][[Category:Bhuvana Ramabhadran of Mt. Kisco NY (US)]]

[[:Category:Andrew Rosenberg of Brooklyn NY (US)|Andrew Rosenberg of Brooklyn NY (US)]][[Category:Andrew Rosenberg of Brooklyn NY (US)]]

[[:Category:Yu Zhang of Mountain View CA (US)|Yu Zhang of Mountain View CA (US)]][[Category:Yu Zhang of Mountain View CA (US)]]

[[:Category:Pedro J. Moreno Mengibar of Jersey City NJ (US)|Pedro J. Moreno Mengibar of Jersey City NJ (US)]][[Category:Pedro J. Moreno Mengibar of Jersey City NJ (US)]]

==USING SPEECH RECOGNITION TO IMPROVE CROSS-LANGUAGE SPEECH SYNTHESIS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18654278 titled 'USING SPEECH RECOGNITION TO IMPROVE CROSS-LANGUAGE SPEECH SYNTHESIS

==Simplified Explanation==
The patent application describes a method for training a speech recognition model using multilingual text-to-speech models. This method involves generating speech representations in different languages conditioned on speaker characteristics, and updating the model based on consistent loss terms.

==Key Features and Innovation==
* Obtaining a multilingual text-to-speech model
* Generating native and cross-lingual synthesized speech representations
* Conditioning on speaker characteristics of native speakers in different languages
* Updating the speech recognition model based on consistent loss terms

==Potential Applications==
This technology can be used in various applications such as language translation, voice assistants, and speech-to-text systems.

==Problems Solved==
This technology addresses the challenges of training speech recognition models in multilingual environments and improving accuracy across different languages.

==Benefits==
* Enhanced speech recognition accuracy in multilingual settings
* Improved performance in recognizing different accents and dialects
* Increased efficiency in training speech recognition models

==Commercial Applications==
* Multilingual voice assistants for global markets
* Language translation services
* Speech-to-text applications for diverse language users

==Prior Art==
Researchers can explore prior art related to multilingual speech recognition models, text-to-speech technology, and speaker adaptation techniques.

==Frequently Updated Research==
Stay informed about the latest advancements in multilingual speech recognition, text-to-speech models, and speaker adaptation methods.

==Questions about Multilingual Speech Recognition==
===How does conditioning on speaker characteristics improve speech recognition accuracy?===
Conditioning on speaker characteristics helps the model adapt to different accents, intonations, and speech patterns, leading to more accurate recognition.

===What are the potential challenges of implementing cross-lingual synthesized speech representations?===
One challenge could be ensuring the accuracy and naturalness of speech across different languages while maintaining consistent speaker characteristics.


==Original Abstract Submitted==

A method for training a speech recognition model includes obtaining a multilingual text-to-speech (TTS) model. The method also includes generating a native synthesized speech representation for an input text sequence in a first language that is conditioned on speaker characteristics of a native speaker of the first language. The method also includes generating a cross-lingual synthesized speech representation for the input text sequence in the first language that is conditioned on speaker characteristics of a native speaker of a different second language. The method also includes generating a first speech recognition result for the native synthesized speech representation and a second speech recognition result for the cross-lingual synthesized speech representation. The method also includes determining a consistent loss term based on the first speech recognition result and the second speech recognition result and updating parameters of the speech recognition model based on the consistent loss term.



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