Parallel Speech-to-Text Framework with Dynamic Engine Routing
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Solution Overview
Problem
Current voice-to-text processing systems face challenges in handling multiple speakers, languages, dialects, accents, industry-specific terms, and context analysis, requiring flexible, scalable, and customizable solutions that can manage complex processing paths and maintain high accuracy and speed.
Innovation Solution
A dynamic framework that configures and reconfigures speech-to-text engines and dictionaries based on audio recording characteristics, utilizing a routing engine, task manager, scaling manager, and error engine to adapt processing paths and resources, enabling parallel and sequential processing to produce accurate transcripts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual transcription methods are used, then accuracy can be maintained through human judgment, but productivity is extremely low and the process is labor-intensive
Solution Approach 1:
The patent replaces manual transcription with automated speech-to-text conversion systems. Multiple speech-to-text engines process audio recordings automatically, converting spoken words to text without human intervention. This substitution of mechanical/automated systems for manual labor dramatically increases productivity while reducing labor intensity.
Solution Approach 2:
The patent divides the transcription task into multiple parallel processing streams using different speech-to-text engines. Each engine processes portions of the audio independently, and results are combined. This segmentation allows simultaneous processing of multiple audio segments, significantly improving overall transcription speed and productivity.
2Productivity
If multiple speech-to-text engines are used in parallel, then productivity increases, but device complexity and difficulty of management increase exponentially
Solution Approach 1:
The patent creates a universal framework that can accommodate multiple different speech-to-text engines through a common interface. The system manages diverse engines (different vendors, different algorithms) through standardized communication protocols and data formats, allowing parallel processing without proportionally increasing management complexity. The framework handles routing, result aggregation, and quality control uniformly across all engines.
Solution Approach 2:
The patent introduces a framework layer as an intermediary between the multiple speech-to-text engines and the final output. This intermediary manages the complexity by handling engine selection, parameter configuration, result validation, and error handling. It shields users from the underlying complexity of managing multiple engines while enabling their coordinated use for improved productivity.
3Adaptability or versatility
If hand-coded routing is used for each processing step, then specific use cases can be handled, but the system becomes difficult to manage and scale to thousands of servers
Solution Approach 1:
The patent implements dynamic routing that automatically selects and configures speech-to-text engines based on audio characteristics rather than requiring hand-coded routing for each case. The system dynamically adjusts processing parameters, engine selection, and resource allocation based on real-time analysis of the audio input. This dynamic approach maintains adaptability to different use cases while enabling automated scaling to thousands of servers without proportional increases in manual configuration.
Solution Approach 2:
The patent changes routing from static hand-coded paths to dynamic parameter-based selection. The framework analyzes audio parameters (language, accent, speaker count, duration) and automatically selects appropriate engines and processing configurations. This parameter-driven approach allows the system to adapt to various use cases while maintaining simplicity for scaling, as the same automated logic handles all cases uniformly.
4Measurement precision
If speech-to-text conversion is performed on entire audio recordings, then complete transcripts are produced, but processing time increases and speed decreases
Solution Approach 1:
The patent segments audio recordings into smaller portions that can be processed in parallel by multiple speech-to-text engines. Each engine processes a specific segment independently, and the results are aggregated into a complete transcript. This segmentation maintains transcription completeness while dramatically improving processing speed through parallel computation and reduced per-engine processing time.
Data Source
AI summary
A method of converting speech to text comprises receiving an audio recording from an input device comprising speech of a plurality of speakers. Extracting from the audio recording, a speaker audio recording comprising recorded audio of an individual speaker. Selecting, based on a characteristic of the speaker audio recording, a speech to text engine and a dictionary. Configuring the speech to text engine with the dictionary and executing a first conversion process to convert a first portion of the speaker audio recording to produce a first transcript. Evaluating a performance metric of the conversion process against a quality metric to reconfigure the speech to text engine and execute a second conversion process to convert a second portion of the speaker audio recording to produce a second transcript. Combining the first transcript and the second transcript to produce a transcript of the speaker audio recording.