18615621. ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION simplified abstract (GOOGLE LLC)

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 18615621 titled 'ALPHANUMERIC SEQUENCE BIASING FOR AUTOMATIC SPEECH RECOGNITION

The patent application discloses 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 applications

1. 1. 1. Problems Solved:

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

1. 1. 1. Benefits:

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

1. 1. 1. Commercial Applications:
         1. Title: Advanced Speech Recognition Technology for Improved User Interfaces

This technology can be utilized in smart speakers, virtual assistants, and transcription services to provide more accurate and contextually relevant results, improving user interactions and overall performance in various industries.

1. 1. 1. Prior Art:

Research on contextual biasing in speech recognition systems and finite state transducers can provide insights into the development of this technology.

1. 1. 1. Frequently Updated Research:

Stay updated on advancements in speech processing algorithms, machine learning models for audio data analysis, and improvements in natural language processing techniques for enhanced speech recognition capabilities.

1. 1. 1. 1. Questions about Speech Processing Techniques:

1. How does the contextual biasing FST improve the accuracy of speech recognition?

  - The contextual biasing FST uses contextual information to adjust probabilities of candidate recognitions, leading to more accurate transcriptions.

2. What are the potential limitations of using speech processing techniques in noisy environments?

  - Noisy environments can impact the quality of audio data, affecting the 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.