SoundHound AI IP, LLC. (20240379092). AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH simplified abstract
Contents
- 1 AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about the Technology
- 1.13 Original Abstract Submitted
AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH
Organization Name
Inventor(s)
Anton V. Relin of Boulder CO (US)
AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240379092 titled 'AUTOMATIC LEARNING OF ENTITIES, WORDS, PRONUNCIATIONS, AND PARTS OF SPEECH
Simplified Explanation
The patent application describes systems that can automatically learn new words, alternate pronunciations, parts of speech, and entity descriptions by analyzing phoneme sequences and sentences.
Key Features and Innovation
- Automatic learning of new words by identifying phoneme sequences that could form successful new words.
- Learning alternate pronunciations by finding phoneme sequences with small edit distances.
- Determining parts of speech by analyzing part-of-speech variations for successful parses.
- Identifying entity descriptions by analyzing sentences for semantic grammar parsing.
Potential Applications
The technology can be applied in automatic speech recognition systems, natural language understanding systems, language learning tools, and text analysis software.
Problems Solved
The technology addresses the challenges of expanding vocabulary, improving pronunciation accuracy, enhancing syntactic parsing, and refining semantic understanding in language processing systems.
Benefits
- Enhanced language learning capabilities.
- Improved accuracy in speech recognition and understanding.
- Increased efficiency in text analysis and parsing.
- Expanded functionality of natural language processing systems.
Commercial Applications
- Integration into virtual assistants for better communication.
- Implementation in language learning apps for improved vocabulary acquisition.
- Utilization in customer service chatbots for enhanced understanding of user queries.
Prior Art
Readers can explore prior research in the fields of automatic speech recognition, natural language processing, phonetics, and computational linguistics for related technologies and advancements.
Frequently Updated Research
Stay updated on advancements in automatic speech recognition, natural language understanding, and machine learning techniques for continuous improvements in language processing technologies.
Questions about the Technology
How does the system determine the part of speech of a word?
The system identifies part-of-speech variations that enable successful parses by syntactic grammars.
What are the potential commercial applications of this technology?
The technology can be utilized in virtual assistants, language learning apps, and customer service chatbots for improved communication and understanding.
Original Abstract Submitted
systems for automatic speech recognition and/or natural language understanding automatically learn new words by finding subsequences of phonemes that, if they were a new word, would enable a successful tokenization of a phoneme sequence. systems can learn alternate pronunciations of words by finding phoneme sequences with a small edit distance to existing pronunciations. systems can learn the part of speech of words by finding part-of-speech variations that would enable parses by syntactic grammars. systems can learn what types of entities a word describes by finding sentences that could be parsed by a semantic grammar but for the words not being on an entity list.
