Client-Server ASR Architecture with Distributed Feature Extraction
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Solution Overview
Problem
There is no standard client-server architecture for Automatic Speech Recognition (ASR) applications in IP-based networks, leading to inefficiencies in time response, computation distribution, network traffic, and scalability.
Innovation Solution
A client-server platform with a Through Feature Streaming Architecture (TFSA) that distributes feature extraction between client and server sides based on computational power and network bandwidth, using WebSockets for efficient data transfer and Message-Oriented Middleware for load balancing, along with a scalable and adaptable architecture.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If feature extraction is performed entirely on the server side, then the client side requires minimal computational power, but the network data traffic increases and time response deteriorates
Solution Approach 1:
The feature extraction process is segmented and distributed between client and server sides. The client performs preliminary feature extraction using a Distributed Frontend, while the server completes the recognition process. This segmentation reduces network data traffic by transmitting only extracted features rather than raw audio streams, and improves time response by performing computational tasks locally at the client.
2Device complexity
If a fixed client-server architecture is used, then the system structure is simple, but the system cannot scale to accommodate growing loads and new functionalities
Solution Approach 1:
The architecture employs dynamic feature extraction where the client's Distributed Frontend can adaptively extract features based on the specific recognition task requirements. The server side dynamically configures recognition channels and adapts to varying computational loads. This dynamic design enables the system to scale flexibly to accommodate growing loads and new functionalities while maintaining manageable complexity.
3Ease of operation
If all ASR processing is centralized on the server, then the client side is simple, but the network bandwidth is consumed excessively
Solution Approach 1:
The client performs preliminary feature extraction actions locally using the Distributed Frontend before transmitting data to the server. This preliminary processing converts raw audio streams into compressed feature vectors, significantly reducing the quantity of data that needs to be transmitted over the network. The server then receives only these pre-processed features for final recognition processing.
Data Source
AI summary
A client-server architecture for Automatic Speech Recognition (ASR) applications, includes: (a) a client-side including: a client being part of distributed front end for converting acoustic waves to feature vectors; VAD for separating between speech and non-speech acoustic signals; adaptor for WebSockets; and (b) a server side including: a web layer utilizing HTTP protocols and including a Web Server having a Servlet Container; an intermediate layer for transport based on Message-Oriented Middleware being a message broker; a recognition server and an adaptation server both connected to said intermediate layer; a Speech processing server; a Recognition Server for instantiation of a recognition channel per client; an Adaptation Server for adaptation acoustic and linguistic models for each speaker; a Bidirectional communication channel between a Speech processing server and client side; and a Persistent layer for storing a Language Knowledge Base connected to said Speech processing server.