Client-Side Speech Domain Detection Using Gyroscopic Data
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Solution Overview
Problem
Cloud-based speech recognition systems face latency issues due to the need for server-side model retrieval and processing, which increases response time and decreases usability, especially as the number of users scales up, as they require significant storage and introduce delays in handling voice requests.
Innovation Solution
Implementing client-side analysis of audio samples using a context recognition engine (CRE) to identify characteristics such as language and environment, allowing for pre-loading of necessary models on the user device, thereby reducing the need for server-side processing and minimizing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cloud-based speech services use server-side model retrieval and processing, then speech recognition can be performed with multiple language models and databases, but response time increases due to model acquisition delays and server processing latencies
Solution Approach 1:
The system performs preliminary actions by pre-loading language models, acoustic models, and other processing components into memory before actual speech recognition requests arrive. This allows the server to immediately begin processing audio samples without delays associated with model acquisition during request handling.
Solution Approach 2:
The system segments the speech recognition workload by separating model storage and processing functions across distributed nodes. Language models are stored on storage nodes while processing occurs on application nodes, allowing parallel operations that reduce sequential processing delays.
2Adaptability or versatility
If a grid manager is used to distribute models among storage and application nodes, then speech services can scale to thousands of clients, but latency increases due to the middleman introducing delays in model retrieval and processing
Solution Approach 1:
The system extracts the model retrieval and processing functions from the centralized grid manager and distributes them directly to application nodes. This eliminates the middleman layer that was introducing delays, allowing application nodes to autonomously load and process models without waiting for grid manager coordination.
Solution Approach 2:
The system dynamically adjusts the distribution of models and processing tasks across application nodes based on real-time demands. Application nodes can independently retrieve and load required models from storage nodes without fixed grid manager coordination, enabling flexible scaling while reducing latency.
3Quantity of substance
If language models are stored on storage nodes and processed on application nodes, then storage costs are reduced through distribution, but response time increases due to the time required to acquire and load models for each voice request
Solution Approach 1:
The system performs preliminary model loading into application node memory before speech recognition requests arrive. This ensures that when audio samples need processing, the required language models are already available in memory, eliminating acquisition time during request handling.
Solution Approach 2:
The system maintains continuous model loading and updating operations in the background, ensuring that application nodes always have the necessary models ready for processing. This continuous preparation eliminates interruptions and delays that would occur with on-demand model acquisition.
Data Source
AI summary
Techniques are disclosed for client-side analysis of audio samples to identify one or more characteristics associated with captured audio. The client-side analysis may then allow a user device, e.g., a smart phone, laptop computer, in-car infotainment system, and so on, to provide the one or more identified characteristics as configuration data to a voice recognition service at or shortly after connection with the same. In turn, the voice recognition service may load one or more recognition components, e.g., language models and/or application modules/engines, based on the received configuration data. Thus, latency may be reduced based on the voice recognition engine having “hints” that allow components to be loaded without necessarily having to process audio samples first. The reduction of latency may reduce processing time relative to other approaches to voice recognitions systems that exclusively perform server-side context recognition/classification.