Speech recognition has become increasingly embedded in our everyday lives with voice-driven application scenarios. Intelligent machine recognition technology can transform speech contents into words using natural language recognition and processing abilities. Coupled with rapid improvements in computer processing, AI-based systems are already improving the accuracy and efficiency of speech recognition across various specializations. Protheragen's machine recognition system can transcribe the medical information into text content and display it in HIS/PACS/CIS systems.
- Electronic Medical Record System
- Doctor-Patient Communication
- Conference Records
Machines need to learn to "listen" to accents, emotions, and inflections, especially terminology used in medical fields. As the technology becomes more sophisticated and more data is used by specific algorithms, those challenges are quickly being overcome.
The premise of accurate machine recognition is high-quality recording equipment.
Another important factor in speech recognition is how to identify and eliminate background noise.
Difficult Accents and Dialects
It is difficult to teach a machine to learn to read a spoken language as humans do.
Varied Pitches of Voices
Human tends to shorten certain words- we do not pronounce them precisely.
Algorithms are based on the architecture of hierarchical explanatory factors and distribution representations, where a cascade of many layers of nonlinear processing units is used for the supervised or unsupervised learning of feature representations per layer, with the layers forming a hierarchy from low-level to high-level features, in the sense of feature extraction and transformation. At the level of speech recognition, we use natural language processing technology to transform unstructured natural language into structured data for subsequent data mining.
Some notable architectures of deep learning include the deep belief networks, convolutional neural networks, and recurrent neural networks. It involves teaching a computer to recognize patterns, rather than programming it with specific rules. The training process involves feeding large amounts of data to the algorithm and allowing it to learn from that data and identify patterns. It mainly uses machine learning platform to invest in language and speech technology, knowledge computing and big data analysis. It can accurately restore the semantics and automatically adjust individual words concerning the context, making the semantics more consistent with oral habits.
Automated Speech Recognition (ASR)
Consumer-centric applications increasingly require ASR to be robust to the full range of real-world noise and other acoustic distorting conditions. ASR traditionally focuses on the recognition of the spoken word on the syntactical level. Protheragen aims to establish a solid, consistent, and common mathematical foundation for robust ASR, emphasizing the methods proven to be successful and expected to sustain or expand their future applicability.
Natural Language Processing (NLP)
Our machine recognition is powered by object recognition, translation, speech recognition, automatic transcription, and natural language processing. As mentioned above, ASR is the conversion of spoken word to text, while NLP is the processing of the text to derive its meaning. Our expert team is dedicated to providing solutions for acoustic models based on Gaussian mixture models(GMM), hidden Markov models (HMM), and deep neural networks(DNN), especially on ASR robustness to noisy acoustic environments.