Distributed ASR on Wearables via Feature Quantization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Integrating automatic speech recognition (ASR) into resource-constrained wearable devices is challenging due to the compute and memory-intensive nature of speech recognition, which limits battery life and accuracy when transmitting raw or compressed audio over short-range wireless connections.
Innovation Solution
A distributed ASR system that uses a wearable device to capture audio and transmit processed audio features, such as mel frequency cepstral coefficients (MFCCs), over Bluetooth Low Energy (BLE) channels to a companion device for further processing, employing efficient quantization and compression techniques to reduce bandwidth and power consumption while maintaining detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If speech recognition is integrated into wearable devices, then speech recognition functionality is achieved, but power consumption increases and battery life decreases
Solution Approach 1:
The system divides speech recognition processing into two segments: resource-constrained wearable devices perform audio capture and feature extraction, while cloud-based servers perform the computationally intensive speech recognition. This segmentation allows the wearable device to maintain speech recognition functionality while consuming minimal power for only the essential local processing tasks.
2Measurement precision
If raw audio is transmitted over wireless connections, then speech recognition accuracy is maintained, but bandwidth usage and power consumption increase
Solution Approach 1:
The system extracts only the essential audio features (such as MFCCs - Mel Frequency Cepstral Coefficients) from the raw audio signal at the wearable device, transmitting only these extracted features to the cloud server. This extraction process maintains speech recognition accuracy by preserving the most relevant acoustic information while dramatically reducing the data volume that needs to be transmitted over the wireless connection.
3Measurement precision
If audio processing is performed locally on wearable devices, then detection accuracy is maintained, but compute and memory resources are exceeded
Solution Approach 1:
The system introduces an intermediary processing stage where the wearable device performs preliminary audio feature extraction using lightweight algorithms, then transmits these processed features to a cloud-based speech recognition server. This intermediary approach allows the wearable device to maintain simple, resource-constrained hardware while still achieving accurate speech recognition through the more powerful cloud infrastructure.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture are disclosed for distributed automatic speech recognition. An example apparatus includes a detector to process an input audio signal and identify a portion of the input audio signal including a sound to be evaluated, the sound to be evaluated organized into a plurality of audio features representing the sound. The example apparatus includes a quantizer to process the audio features using a quantization process to reduce the audio features to generate a reduced set of audio features for transmission. The example apparatus includes a transmitter to transmit the reduced set of audio features over a low-energy communication channel for processing.