Local ASR Device Arbitration for Noisy Speech Recognition
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Solution Overview
Problem
Existing automatic speech recognition (ASR) systems often struggle with accuracy due to varying audio quality and noise levels across different devices, leading to inefficient and inaccurate human-computer interactions.
Innovation Solution
A device arbitration technique that selectively utilizes multiple client devices in a local environment to generate candidate text representations of spoken utterances, leveraging their hardware and software capabilities to enhance accuracy and efficiency by balancing computational and network resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single client device performs local ASR processing, then device complexity is reduced and ease of operation is improved, but speech recognition accuracy deteriorates due to varying audio quality and noise levels
Solution Approach 1:
The system segments the ASR task across multiple client devices, where each device independently processes audio data using its locally stored ASR model to generate candidate text representations. This division of labor allows the system to leverage the strengths of multiple devices while maintaining local processing capabilities, thereby improving speech recognition accuracy without requiring a single complex device to handle all processing alone.
Solution Approach 2:
The system merges the results from multiple candidate text representations generated by different client devices through a result combination module. By combining these candidates and selecting the most accurate representation, the system achieves higher speech recognition accuracy than any single device could provide alone, while distributing the computational complexity across multiple devices.
2Measurement precision
If multiple client devices are utilized for ASR processing, then speech recognition accuracy is improved by leveraging diverse hardware and software capabilities, but computational and network resources are consumed
Solution Approach 1:
The system employs partial action by selectively transmitting audio data to only those additional client devices that are necessary to improve accuracy above a threshold level. Not all available devices are engaged for every ASR task, which reduces unnecessary computational and network resource consumption while still achieving the desired accuracy improvement when needed.
Solution Approach 2:
The system changes parameters such as the number of devices involved, the transmission of audio data, and the selection of candidate results dynamically based on accuracy requirements and resource availability. This allows the system to optimize the balance between speech recognition accuracy and computational resource consumption by adjusting its operation mode according to current conditions.
3Measurement precision
If audio data is transmitted to additional client devices for processing, then speech recognition accuracy is improved through multiple candidate generations, but network communication overhead increases
Solution Approach 1:
The system extracts and transmits only the essential audio data to additional client devices, rather than transmitting redundant information. This minimizes the network communication overhead while still providing sufficient information for those devices to generate accurate candidate text representations, thereby improving speech recognition accuracy without excessive energy loss to network communication.
4Measurement precision
If local ASR models are stored on multiple devices, then speech recognition accuracy is improved through distributed processing, but device storage requirements increase
Solution Approach 1:
The ASR model stored on each client device serves multiple functions: it enables local speech recognition processing, allows the device to contribute candidate text representations to the distributed system, and provides redundancy that improves overall system accuracy. This multi-functionality justifies the storage capacity requirement, as the same model data delivers multiple benefits across the distributed system.
Data Source
AI summary
A text representation of a spoken utterance can be generated based on a candidate text representation of a spoken utterance generated using a given client device and/or based on one or more additional candidate text representations of the spoken utterance each generated using a corresponding additional client device. Various implementations include determining the additional client device(s) from a set of additional client devices in an environment with the given client device. Various implementations additionally or alternatively include determining whether an additional client device is to generate an additional candidate text representation of the spoken utterance based on audio data captured by microphone(s) of the given client device and/or based on additional audio data that captured by microphone(s) of the additional client device.