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Diffstat (limited to 'tensorflow/contrib/lite/models/testdata/g3doc/README.md')
| -rw-r--r-- | tensorflow/contrib/lite/models/testdata/g3doc/README.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/tensorflow/contrib/lite/models/testdata/g3doc/README.md b/tensorflow/contrib/lite/models/testdata/g3doc/README.md index 667a588383..1c47e00aae 100644 --- a/tensorflow/contrib/lite/models/testdata/g3doc/README.md +++ b/tensorflow/contrib/lite/models/testdata/g3doc/README.md @@ -53,7 +53,7 @@ with the corresponding parameters as shown in the figure. ### Automatic Speech Recognizer (ASR) Acoustic Model (AM) The acoustic model for automatic speech recognition is the neural network model -for matching phonemes to the input autio features. It generates posterior +for matching phonemes to the input audio features. It generates posterior probabilities of phonemes from speech frontend features (log-mel filterbanks). It has an input size of 320 (float), an output size of 42 (float), five LSTM layers and one fully connected layers with a Softmax activation function, with @@ -68,7 +68,7 @@ for predicting the probability of a word given previous words in a sentence. It generates posterior probabilities of the next word based from a sequence of words. The words are encoded as indices in a fixed size dictionary. The model has two inputs both of size one (integer): the current word index and -next word index, an output size of one (float): the log probability. It consits +next word index, an output size of one (float): the log probability. It consists of three embedding layer, three LSTM layers, followed by a multiplication, a fully connected layers and an addition. The corresponding parameters as shown in the figure. |