Distributed Speech Middleware for Scalable ASR Latency
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Solution Overview
Problem
Mobile automatic speech recognition (ASR) systems face challenges in scalability and performance under peak loads due to high latency and resource constraints, particularly in varying acoustic environments, where traditional server implementations degrade significantly as the number of clients increases.
Innovation Solution
A distributed speech-enabled middleware (DSEM) framework that uses event-driven, non-blocking I/O models and decoder caching to manage multiple clients efficiently, along with unsupervised acoustic normalization and adaptation techniques to reduce word error rates, allowing for robust ASR services even under heavy loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional server implementations are used to handle multiple clients, then the system can serve more users, but latency increases and performance degrades under peak loads
Solution Approach 1:
The system segments speech processing into two independent components: feature extraction (performed by mobile devices) and decoding (performed by server). This segmentation allows the server to process multiple clients concurrently without each client blocking the entire system, reducing latency while maintaining scalability.
Solution Approach 2:
The mobile device performs feature extraction on speech audio data before transmitting to the server. This preliminary action prepares the data in advance, allowing the server to immediately begin decoding without waiting for feature extraction, thereby reducing overall latency and improving throughput under peak loads.
2Reliability
If more ASR resources are allocated to handle increased client load, then service quality is maintained, but system cost and complexity increase
Solution Approach 1:
Mobile devices perform self-service by executing feature extraction locally using embedded ASR libraries. This distributes the computational burden away from the server, allowing the system to handle more clients without proportionally increasing server resources or complexity while maintaining service quality.
Solution Approach 2:
The system uses a universal decoder on the server that can process features from multiple clients simultaneously. This single multi-functional decoder replaces what would otherwise require multiple dedicated decoders, reducing system complexity and resource requirements while maintaining reliability across all clients.
3Measurement precision
If acoustic normalization and adaptation techniques are applied, then word error rate is reduced, but processing time increases
Solution Approach 1:
Acoustic normalization and adaptation are performed as preliminary processing steps on the speech audio data before decoding. By preparing the acoustic features in advance, the actual decoding process becomes faster and more accurate, reducing overall processing time while improving word error rate.
Solution Approach 2:
The patent introduces acoustic normalization and adaptation as intermediary processing steps between feature extraction and decoding. These intermediary techniques transform the raw features into a standardized form that is more suitable for decoding, improving accuracy without requiring additional computational resources during the critical decoding phase.
Data Source
AI summary
A digital speech enabled middleware module is disclosed that facilitates interaction between a large number of client devices and network-based automatic speech recognition (ASR) resources. The module buffers feature vectors associated with speech received from the client devices when the number of client devices is greater than the available ASR resources. When an ASR decoder becomes available, the module transmits the feature vectors to the ASR decoder and a recognition result is returned.