Unified Speech Recognition Text for Low-Latency Conversational AI
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Solution Overview
Problem
Conventional automatic speech recognition systems output unreliable text with errors and require multiple sequential processing components, leading to increased latency and error propagation.
Innovation Solution
A single machine learning model is trained to generate unified text with punctuation, capitalization, end-of-sentence, and end-of-utterance detections, reducing the need for multiple systems and minimizing errors and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sequential systems are used to process speech data (ASR, punctuation, capitalization, ITN, EOS/EOU detection), then comprehensive text processing functionality is achieved, but overall latency increases and error propagation occurs
Solution Approach 1:
The patent combines multiple separate text processing systems (ASR, punctuation insertion, capitalization correction, ITN, and EOS/EOU detection) into a single unified machine learning model. This model processes speech data and generates fully formatted text with all these features simultaneously, eliminating the sequential processing delays and error propagation that occurred when multiple independent systems were chained together.
Solution Approach 2:
The unified machine learning model performs multiple text processing functions within a single system. It simultaneously conducts automatic speech recognition, inserts punctuation marks, applies capitalization rules, performs inverse text normalization, and detects end-of-sentence and end-of-utterance points, thereby achieving comprehensive text processing functionality without requiring separate specialized systems for each task.
2Adaptability or versatility
If multiple sequential systems are used to process speech data, then comprehensive text processing functionality is achieved, but device complexity increases
Solution Approach 1:
The patent consolidates multiple independent processing components into a single unified machine learning model architecture. Instead of maintaining separate systems for ASR, punctuation, capitalization, ITN, and EOS/EOU detection, the unified model integrates all these functionalities into one coherent system, thereby reducing architectural complexity while preserving comprehensive text processing capabilities.
Solution Approach 2:
The unified machine learning model is designed to perform multiple text processing functions simultaneously within a single system. This multi-functional approach eliminates the need for complex inter-system communications and coordination between multiple specialized components, thereby simplifying the overall system architecture while maintaining versatile text processing capabilities.
3Adaptability or versatility
If multiple sequential systems are used to process speech data, then comprehensive text processing functionality is achieved, but error propagation increases
Solution Approach 1:
The patent merges multiple error-prone sequential processing stages into a single unified machine learning model. By processing speech data through one integrated system rather than chaining multiple independent systems, the model eliminates error propagation that occurs when mistakes from one system are passed to the next. The unified model generates consistent, error-free output across all text processing functions simultaneously.
Data Source
AI summary
In various examples, generating unified text using speech recognition models for AI systems and applications is described herein. Systems and methods are disclosed that use a machine learning model that is trained to generate unified text associated with user speech, where the unified text includes punction marks, capitalizations of words, inverse text normalization formatting, end of sentence (EOS) detections, and/or end of utterance (EOU) detections. For instance, the machine learning model may receive audio data representing speech as input. The machine learning model may then process the audio data and, based at least on the processing, generate output data associated with the speech. In some examples, the output data may represent tokens, such as tokens associated with automatic speech recognition processing, punctuation and capitalization processing, EOS and/or EOU processing, and/or inverse text normalization processing. In such examples, the tokens may then be processed to generate the unified text.