From d2b9853ca848f11dee55beedbb9d650763b3ed53 Mon Sep 17 00:00:00 2001 From: George Gekov Date: Thu, 12 Aug 2021 13:23:16 +0100 Subject: Fix typos Signed-off-by: George Gekov Change-Id: I24c64064874b2e6baabdbff9c762972b816fc272 --- docs/use_cases/kws_asr.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'docs/use_cases/kws_asr.md') diff --git a/docs/use_cases/kws_asr.md b/docs/use_cases/kws_asr.md index 8b2f123..8d7b396 100644 --- a/docs/use_cases/kws_asr.md +++ b/docs/use_cases/kws_asr.md @@ -52,7 +52,7 @@ Therefore, this section aims to provide an overview of the feature extraction pr First, the audio data is normalized to the range (`-1`, `1`). > **Note:** Mel-Frequency Cepstral Coefficients (MFCCs) are a common feature that is extracted from audio data and can -> be used as input for machine learning tasks. Such as keyword spotting and speech recognition. For implementation +> be used as input for machine learning tasks such as keyword spotting and speech recognition. For implementation > details, please refer to: `source/application/main/include/Mfcc.hpp` Next, a window of 640 audio samples is taken from the start of the audio clip. From these 640 samples, we calculate 10 -- cgit v1.2.1