speech_recognition/esp-sr
1
0
Fork 0
mirror of https://github.com/espressif/esp-sr.git synced 2025-09-15 15:28:44 +08:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity

Update README

Browse Source
This commit is contained in:
Sun Xiang Yu 2019-08-21 16:09:03 +08:00
parent 684503d050
commit c7167defef
2 changed files with 12 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
README.md
View File

@ -1,9 +1,10 @@
# esp_sr
Espressif esp_sr provides basic algorithms for **Speech Recognition** applications. Now, this framework has two models:
Espressif esp_sr provides basic algorithms for **Speech Recognition** applications. Now, this framework has three modules:
* The wake word detection model [WakeNet](wake_word_engine/README.md)
* The speech command recognition model [MultiNet](speech_command_recognition/README.md)
* Acoustic algorithm: AEC(Acoustic Echo Cancellation), VAD(Voice Activity Detection), AGC(Automatic Gain Control), NS(Noise Suppression)
These algorithms are provided in the form of a component, so they can be integrated into your projects with minimum efforts.

20
wake_word_engine/README.md
View File

@ -18,7 +18,7 @@ Please see the flow diagram of WakeNet below:
- WakeNet1 and WakeNet2 had been out of use.
- WakeNet3 and WakeNet4 are built upon the [CRNN](https://arxiv.org/abs/1703.05390) structure.
- WakeNet5(WakeNet5X2,WakeNetX3) and WakeNet6 are built upon the [Dilated Convolution](https://arxiv.org/pdf/1609.03499.pdf) structure.
Note that，The network structure of WakeNet5,WakeNet5X2 and WakeNet5X3 is same, but the parameter of WakeNetX2 and WakeNetX3 is more than WakeNet5. Please refer to [Resource Occupancy](#Resource Occupancy) for details.
Note that，The network structure of WakeNet5,WakeNet5X2 and WakeNet5X3 is same, but the parameter of WakeNetX2 and WakeNetX3 is more than WakeNet5. Please refer to [Resource Occupancy](#performance-test) for details.
- Keyword Triggering Method
@ -67,24 +67,24 @@ Please see the flow diagram of WakeNet below:
### 1. Resource Occupancy(ESP32)
|Model Type|Parameter Size|RAM|Average Running Time per Frame| Frame Length|
|Model Type|Parameter Num|RAM|Average Running Time per Frame| Frame Length|
|:---:|:---:|:---:|:---:|:---:|
|Quantized WakeNet3|26 K|20 KB|29 ms|90 ms|
|Quantised WakeNet4|53 K|22 KB|48 ms|90 ms|
|Quantised WakeNet5|41 K|15 KB|7 ms|30 ms|
|Quantised WakeNet5X2|41 K|15 KB|7 ms|30 ms|
|Quantised WakeNet5X3|41 K|15 KB|7 ms|30 ms|
|Quantised WakeNet5|41 K|15 KB|5.5 ms|30 ms|
|Quantised WakeNet5X2|165 K|20 KB|10.5 ms|30 ms|
|Quantised WakeNet5X3|371 K|24 KB|18 ms|30 ms|
### 2. Performance
|Distance| Quiet | Stationary Noise (SNR = 5 ~ 10 dB)| Speech Noise (SNR = 5 ~ 10 dB)| AEC Interruption (-5 ~ -10 dB)|
|Distance| Quiet | Stationary Noise (SNR = 5 dB)| Speech Noise (SNR = 5 dB)| AEC Interruption (-10 dB)|
|:---:|:---:|:---:|:---:|:---:|
|1 m|97%|90%|88%|89%|
|3 m|95%|85%|75%|73%|
|1 m|95%|88%|85%|89%|
|3 m|90%|80%|75%|80%|
False triggering rate: 1 time in 20 hours
False triggering rate: 1 time in 12 hours
**Note**: We use the ESP32-LyraT-Mini development board and the WakeNet5 model in our test. The performance is limited because ESP32-LyraT-Mini only has one microphone. We expect a better recognition performance when more microphones are involved in the test.
**Note**: We use the ESP32-LyraT-Mini development board and the WakeNet5X2(hilexin) model in our test. The performance is limited because ESP32-LyraT-Mini only has one microphone. We expect a better recognition performance when more microphones are involved in the test.
## Wake Word Customization