Google llc (20240185844). CONTEXT-AWARE END-TO-END ASR FUSION OF CONTEXT, ACOUSTIC AND TEXT PRESENTATIONS simplified abstract
CONTEXT-AWARE END-TO-END ASR FUSION OF CONTEXT, ACOUSTIC AND TEXT PRESENTATIONS
Organization Name
Inventor(s)
Shuo-yiin Chang of Sunnyvale CA (US)
CONTEXT-AWARE END-TO-END ASR FUSION OF CONTEXT, ACOUSTIC AND TEXT PRESENTATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185844 titled 'CONTEXT-AWARE END-TO-END ASR FUSION OF CONTEXT, ACOUSTIC AND TEXT PRESENTATIONS
Simplified Explanation
The method described in the abstract involves using an automatic speech recognition (ASR) model to process acoustic frames of an input utterance and generate a transcription. This is achieved through various components of the ASR model, including an audio encoder, context encoder, prediction network, and joint network.
- The audio encoder generates a higher order feature representation for each acoustic frame in the input utterance sequence.
- The context encoder generates a context embedding based on previous transcriptions output by the ASR model.
- The prediction network produces a dense representation from a sequence of non-blank symbols.
- The joint network generates a probability distribution over possible speech recognition hypotheses using the context embeddings, higher order feature representations, and dense representations.
Potential Applications: - Automatic speech recognition systems - Voice-controlled devices - Transcription services
Problems Solved: - Improving accuracy and efficiency of speech recognition - Enhancing transcription capabilities - Streamlining voice command technology
Benefits: - Faster and more accurate transcription of spoken language - Enhanced user experience with voice-controlled devices - Increased productivity in various industries reliant on transcription services
Potential Commercial Applications: - Speech-to-text software for businesses - Voice-activated virtual assistants - Transcription services for media and entertainment industries
Possible Prior Art: - Existing automatic speech recognition models and systems - Previous research on audio encoding and transcription technologies
- Unanswered Questions
- How does this method compare to traditional speech recognition models?
- Unanswered Questions
This article does not directly compare the performance of this method to traditional speech recognition models. Further research or testing may be needed to determine the advantages and limitations of this approach.
- What are the potential limitations of this method in real-world applications?
The article does not discuss any potential limitations or challenges that may arise when implementing this method in real-world scenarios. Additional studies or case studies could provide insights into the practicality and effectiveness of this technology.
- Frequently Updated Research
At the moment, there is no specific information available on frequently updated research related to this method.
Original Abstract Submitted
a method includes receiving a sequence of acoustic frames characterizing an input utterance and generating a higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames by an audio encoder of an automatic speech recognition (asr) model. the method also includes generating a context embedding corresponding to one or more previous transcriptions output by the asr model by a context encoder of the asr model and generating, by a prediction network of the asr model, a dense representation based on a sequence of non-blank symbols output by a final softmax layer. the method also includes generating, by a joint network of the asr model, a probability distribution over possible speech recognition hypotheses based on the context embeddings generated by the context encoder, the higher order feature representation generated by the audio encoder, and the dense representation generated by the prediction network.
