International Business Machines Corporation (20240274125). ADAPTABLE ACOUSTIC MODEL BUILT WITH LIMITED LABELING DATA simplified abstract
Contents
ADAPTABLE ACOUSTIC MODEL BUILT WITH LIMITED LABELING DATA
Organization Name
International Business Machines Corporation
Inventor(s)
Yi Chen Zhong of Shanghai (CN)
Yuan Yuan Ding of Shanghai (CN)
ADAPTABLE ACOUSTIC MODEL BUILT WITH LIMITED LABELING DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240274125 titled 'ADAPTABLE ACOUSTIC MODEL BUILT WITH LIMITED LABELING DATA
The abstract describes a method, computer system, and computer program product for building an acoustic model. The process involves contrastive pre-training, dataset classifier building, prediction process, and zero-shot audio prediction using a pre-trained acoustic model.
- Contrastive pre-training of the acoustic model
- Building a dataset classifier through prompt engineering
- Performing a prediction process
- Zero-shot audio prediction using the pre-trained acoustic model
Potential Applications: - Speech recognition systems - Voice-controlled devices - Language translation tools
Problems Solved: - Improving accuracy of acoustic models - Enhancing performance of speech recognition systems
Benefits: - Increased efficiency in audio prediction - Enhanced accuracy in speech recognition - Improved user experience with voice-controlled devices
Commercial Applications: Title: "Advanced Acoustic Model for Speech Recognition Systems" This technology can be utilized in industries such as telecommunications, customer service, and smart home devices. It can improve the accuracy and speed of speech recognition systems, leading to better customer interactions and user experiences.
Prior Art: Readers can explore prior art related to acoustic models, speech recognition systems, and machine learning algorithms to understand the evolution of this technology.
Frequently Updated Research: Researchers are constantly exploring new techniques and algorithms to enhance acoustic models and speech recognition systems. Stay updated on advancements in machine learning and audio processing technologies.
Questions about Acoustic Models: 1. How does contrastive pre-training improve the performance of acoustic models? Contrastive pre-training helps the model learn better representations by distinguishing between positive and negative examples, leading to improved accuracy in predictions.
2. What are the key challenges in zero-shot audio prediction using pre-trained models? Zero-shot audio prediction faces challenges in generalizing to unseen data and maintaining accuracy without fine-tuning on specific tasks.
Original Abstract Submitted
according to one embodiment, a method, computer system, and computer program product for building an acoustic model is provided. the present invention may include performing contrastive pre-training of the acoustic model; building a dataset classifier using prompt engineering; performing a prediction process; and performing zero-shot audio prediction using the pre-trained acoustic model.
