SOUNDHOUND AI IP, LLC

Technologies for generating artificial speech that is personalized, context-aware, or adaptable to specific virtual agents or messaging campaigns, often utilizing text-to-speech (TTS) and audio caching for efficient delivery.

AI systems designed to engage in natural language dialogue, maintain conversation state, understand user intent, and generate relevant responses, often across multiple communication channels or modalities.

Application of neural networks and AI/ML models within hearing devices or audio systems to enhance, filter, or process audio signals for improved user experience, such as speech understanding or noise reduction.

Applications of speech processing and artificial intelligence for medical diagnosis, therapeutic interventions, or accessibility solutions, particularly for conditions affecting speech production or hearing.

Systems and methods utilizing artificial intelligence, particularly large language models and neural networks, to extract, summarize, generate, or categorize information from unstructured or semi-structured data sources.

Techniques to improve the accuracy and robustness of Automatic Speech Recognition (ASR) systems by incorporating contextual information, dynamic hint words, or customized machine learning models for specific domains or users.

Techniques for enhancing, encoding, decoding, or separating speech and audio signals, often involving multi-microphone arrays, acoustic echo cancellation, beamforming, or advanced audio compression for improved clarity and quality.

Methods and systems for identifying synthetic or manipulated speech (deepfake audio) using forensic analysis of audio features, such as breath patterns, vocoder signatures, or machine learning models to determine authenticity.

Systems that process data to provide personalized recommendations, predict events, or automate decision-making processes based on learned patterns, user behavior, or environmental factors.

Techniques for generating human-like text or other content using large pre-trained models, often involving prompt engineering, speculative decoding, or multi-modal inputs for content creation.

Utilizing machine learning, particularly deep learning, to analyze medical data such as images, sensor readings, or physiological signals for disease prediction, diagnosis, or treatment assessment.

Techniques employed within compilers or related tools to analyze program code, identify entities for compilation, and optimize execution on target hardware, including reconfigurable systems, to improve performance or resource efficiency.