GB/T 41813.1-2022 in English
VALIDInformation technology—Intelligent speech interaction testing method—Part 1：Speech recognition
- Issued on:2022-10-12
- Implemented on:2023-05-01
- File Format:PDF
- Delivery:Via email within 1~3 business days
$243.00
《GB/T 41813.1-2022信息技术 智能语音交互测试方法 第1部分：语音识别》由TC28（全国信息技术标准化技术委员会）归口，TC28SC35（全国信息技术标准化技术委员会用户界面分会）执行，主管部门为国家标准化管理委员会。
Introduction
Standard Background and Evolution Path
This standard takes the speech recognition system as the core test object, and simultaneously supports the technical specifications of the GB/T36464 series in more than 20 application scenarios such as smart home and vehicle systems. Compared with the early speech quality evaluation specifications, new key technical requirements such as multi-language mixed recognition and high noise scene simulation are added.
Comparison table of key test categories
| Test classification | Core indicators | Innovation points | Meeting standards |
|---|---|---|---|
| Functional test | Number of wake-up words supported, consistency of feature encoding | Requires simultaneous monitoring of ≥3 audio streams | DTU error≤1.5% |
| Performance test | Average response time, attenuation gain | Tested in a strong noise environment (S/N <5dB) | SNR improvement≥15dB |
Technical requirements for test environment
Specification case of audio acquisition equipment
Typical test bench needs to be configured with:
- 64-channel bionic ear array (frequency response range 80Hz-18kHz)
- Adaptive ±25dB dynamic gain controller
- Signal-to-noise ratio compensation unit (covering AC weighted noise samples)
*The example verification shows that under 70dB(A) factory noise interference, the use of ISO3382 standard reflective screen can improve the speaker separation accuracy by 12.8%.
Implementation Suggestions
Five-step System Adaptation Method
- Test Classification and Grading: Configure noise reduction strategy according to Class D voice scenario
- Computing Resource Reservation: Mono processing requires pre-allocation of ≥2 CPU cores
- Multi-dimensional Verification Strategy: Requires compatibility with RTOS and Linux dual-platform verification
- Abnormal Acoustic Modeling: Establish ARCSIN transformation model to compensate for burst impulse noise
- Cross-platform consistency check: Requires Android/iOS decoding delay difference of <50ms
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