Voice Ordering Interface for Multi-Speaker POS Accuracy
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Solution Overview
Problem
Existing point-of-sale systems for eateries lack efficient remote order processing capabilities, particularly in handling multiple contributing sources of audio data and distinguishing between speakers, leading to inaccuracies in order fulfillment.
Innovation Solution
A system utilizing dual voice recognition processors to independently process audio data, selecting the highest quality result for language and entity recognition, and integrating with cloud-based computing for order execution, enabling remote and drive-through ordering through voice commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single voice recognition processor is used for order processing, then device complexity is reduced, but order accuracy deteriorates due to inability to differentiate between multiple audio sources and speakers
Solution Approach 1:
The system segments the audio processing task by employing multiple specialized voice recognition processors (first processor for transcription, second processor for entity recognition) rather than using a single general-purpose processor. This segmentation allows each processor to specialize in specific aspects of audio analysis, improving overall order accuracy while maintaining manageable system complexity through clear division of labor.
Solution Approach 2:
The system introduces an intermediary component that receives audio data from multiple sources, processes it through different voice recognition processors, and integrates the results. This intermediary layer coordinates between multiple processors and the order fulfillment system, enabling accurate differentiation of multiple speakers and audio sources without requiring direct complex interactions between all system components.
2Measurement precision
If multiple voice recognition processors are employed to differentiate audio sources, then order accuracy is improved, but processing time increases due to multiple independent processing operations
Solution Approach 1:
The system performs preliminary actions by having the first voice recognition processor conduct transcription processing before the second processor performs entity recognition. This sequential preliminary processing allows each processor to focus on its specialized task without redundant operations, improving speaker differentiation accuracy while minimizing overall processing time through efficient task sequencing.
Solution Approach 2:
The system implements dynamic processing where the second voice recognition processor selectively operates based on whether the first processor has supplied sufficient indication. This dynamic approach allows the system to adapt processing depth to the complexity of each audio input, reducing unnecessary processing time for simple cases while maintaining high accuracy for complex multi-speaker scenarios.
3Measurement precision
If comprehensive language processing is performed on all audio data, then entity recognition accuracy is improved, but computational resources are wasted on low-quality transcriptions from secondary processors
Solution Approach 1:
The system applies local quality by assigning different processing qualities to different processors based on their specialized functions. The first processor provides high-quality transcription for basic text conversion, while the second processor provides high-quality entity recognition only when needed. This localized quality assignment ensures computational resources are concentrated where they provide maximum value, avoiding waste on redundant high-quality processing of already-sufficient data.
Solution Approach 2:
The system changes processing parameters dynamically by having the second voice recognition processor activate comprehensive language processing only when the first processor has not supplied sufficient indication. This parameter change approach allows the system to adjust processing intensity based on input complexity, ensuring high entity recognition accuracy for challenging audio while conserving computational resources for simpler cases that don't require full processing.
Data Source
AI summary
A non-transitory computer-readable medium is configured to store instructions executable by one or more processors to perform operations including receiving, via an audio signal interface, audio data, processing the audio data to identify one or more speakers from which the audio data is produced, determining, from the processed audio data, an intention to place one or more orders, and communicating, in response to determining the intention, with at least one point-of-sale (POS) terminal to execute order processing.