diff options
| -rw-r--r-- | docs/use_cases/asr.md | 2 | ||||
| -rw-r--r-- | docs/use_cases/kws.md | 2 | ||||
| -rw-r--r-- | docs/use_cases/kws_asr.md | 2 |
3 files changed, 3 insertions, 3 deletions
diff --git a/docs/use_cases/asr.md b/docs/use_cases/asr.md index 0969b63..134a706 100644 --- a/docs/use_cases/asr.md +++ b/docs/use_cases/asr.md @@ -32,7 +32,7 @@ This section provides an overview of the feature extraction process used. 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 512 audio samples is taken from the start of the audio clip. From these 512 samples, we calculate 13 diff --git a/docs/use_cases/kws.md b/docs/use_cases/kws.md index d6bfc3a..13ce7c3 100644 --- a/docs/use_cases/kws.md +++ b/docs/use_cases/kws.md @@ -31,7 +31,7 @@ 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 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 |