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.

1. 1. 1. Potential Applications:

- Speech-to-text transcription services - Voice-controlled devices - Language translation tools

1. 1. 1. Problems Solved:

- Enhances accuracy of speech recognition systems - Improves transcription quality in noisy environments

1. 1. 1. Benefits:

- Increased efficiency in converting audio to text - Better user experience with voice-activated technologies

1. 1. 1. Commercial Applications:
         1. Title: Advanced Speech Recognition Technology for Enhanced User Experience

This technology can be utilized in various industries such as: - Customer service for automated call centers - Legal and medical transcription services - Language learning applications

1. 1. 1. Questions about Speech Processing Techniques:
         1. 1. How does the contextual biasing FST improve the accuracy of speech recognition?

The contextual biasing FST uses contextual information to refine the recognition of alphanumeric sequences, leading to more accurate transcriptions.

1. 1. 1. 1. 2. What are the potential limitations of using this technology in real-world applications?

While this technology improves accuracy, it may still face challenges in accurately transcribing complex or specialized vocabulary.

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.