18493268. METHOD FOR SPEECH-TO-SPEECH CONVERSION simplified abstract (GOOGLE LLC)
Contents
- 1 METHOD FOR SPEECH-TO-SPEECH CONVERSION
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHOD FOR SPEECH-TO-SPEECH CONVERSION - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Unanswered Questions
- 1.11 Original Abstract Submitted
METHOD FOR SPEECH-TO-SPEECH CONVERSION
Organization Name
Inventor(s)
Oleg Rybakov of Redmond WA (US)
Fadi Biadsy of Sandyston NJ (US)
METHOD FOR SPEECH-TO-SPEECH CONVERSION - A simplified explanation of the abstract
This abstract first appeared for US patent application 18493268 titled 'METHOD FOR SPEECH-TO-SPEECH CONVERSION
Simplified Explanation
The present disclosure relates to a streaming speech-to-speech conversion model, where an encoder runs in real time while a user is speaking, then after the speaking stops, a decoder generates output audio in real time. A streaming-based approach produces an acceptable delay with minimal loss in conversion quality when compared to other non-streaming server-based models. A hybrid model approach combines look-ahead in the encoder and a non-causal stacker with non-causal self-attention.
- Encoder runs in real time while user is speaking
- Decoder generates output audio in real time after speaking stops
- Streaming-based approach with minimal delay and quality loss
- Hybrid model combines look-ahead in encoder and non-causal stacker with non-causal self-attention
Potential Applications
This technology can be applied in real-time translation services, virtual assistants, and language learning tools.
Problems Solved
This technology solves the problem of delays in speech-to-speech conversion and maintains high conversion quality.
Benefits
The benefits of this technology include real-time conversion, minimal delay, and high-quality output audio.
Potential Commercial Applications
Potential commercial applications of this technology include speech translation apps, virtual meeting platforms, and language tutoring services.
Possible Prior Art
One possible prior art could be traditional speech-to-text models that do not operate in real time.
Unanswered Questions
How does this technology handle different accents and speech patterns?
The article does not address how the streaming speech-to-speech conversion model adapts to various accents and speech patterns.
What is the energy consumption of this technology compared to non-streaming models?
The article does not provide information on the energy consumption of the streaming speech-to-speech conversion model in comparison to non-streaming models.
Original Abstract Submitted
The present disclosure relates to a streaming speech-to-speech conversion model, where an encoder runs in real time while a user is speaking, then after the speaking stops, a decoder generates output audio in real time. A streaming-based approach produces an acceptable delay with minimal loss in conversion quality when compared to other non-streaming server-based models. A hybrid model approach for combines look-ahead in the encoder and a non-causal stacker with non-causal self-attention.
