GOOGLE LLC (20240233732). ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION simplified abstract

ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION

Organization Name

GOOGLE LLC

Inventor(s)

Benjamin Haynor of New York NY (US)

Petar Aleksic of Jersey City NJ (US)

ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240233732 titled 'ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION

The patent application discusses speech processing techniques that can determine a text representation of alphanumeric sequences in captured audio data.

• Contextual biasing finite state transducer (FST) is determined based on contextual information from the audio data.
• Probabilities of candidate recognitions of the alphanumeric sequence are modified using the contextual biasing FST.

Potential Applications: - Speech-to-text transcription services - Voice-controlled devices - Language translation applications

Problems Solved: - Improving accuracy of speech recognition systems - Enhancing contextual understanding in audio data processing

Benefits: - Increased efficiency in transcribing audio data - Enhanced user experience in voice-activated technologies

Commercial Applications: Title: Advanced Speech Recognition Technology for Enhanced User Experience This technology can be utilized in various industries such as telecommunications, customer service, and transcription services to improve accuracy and efficiency in speech processing.

Questions about Speech Processing Techniques: 1. How do speech processing techniques impact the accuracy of transcription services?

  - Speech processing techniques play a crucial role in improving the accuracy of transcription services by enabling the conversion of audio data into text with contextual understanding.

2. What are the potential implications of using contextual biasing FST in speech recognition systems?

  - The use of contextual biasing FST can lead to more accurate and contextually relevant transcriptions, enhancing the overall performance of speech recognition systems.

Original Abstract Submitted

speech processing techniques are disclosed that enable determining a text representation of alphanumeric sequences in captured audio data. various implementations include determining a contextual biasing finite state transducer (fst) based on contextual information corresponding to the captured audio data. additional or alternative implementations include modifying probabilities of one or more candidate recognitions of the alphanumeric sequence using the contextual biasing fst.