Supervised Learning and Speech Recognition.pptx

supervised learning and speech recognition

1. Supervised Learning and Speech Recognition BY PALLAVI.N

2. CONTENTS >SUPERVISED LEARNING >HOW IT WORKS >EVALUATING SUPERVISED LEARNING > EXAMPLES >SPEECH RECOGNITION

3. SUPERVISED LEARNING Supervised learning, also known as supervised machine learning, is a subcategory of machine learning and artificial intelligence. It is defined by its use of labeled data sets to train algorithms that to classify data or predict outcomes accurately.

4. HOW IT WORKS Supervised learning uses a training set to teach models to yield the desired output. This training dataset includes inputs and correct outputs, which allow the model to learn over time. The data used in supervised learning is labeled — meaning that it contains examples of both inputs (called features) and correct outputs (labels). The algorithms analyze a large dataset of these training pairs to infer what a desired output value would be when asked to make a prediction on new data.based on the previous knowledge it has learned.

5. EVALUATION OF SUPERVISED LEARNING Evaluating supervised learning models is an important step in ensuring that the model is accurate and generalizable. There are a number of different metrics that can be used to evaluate supervised learning models, but some of the most common ones include: For Regression Mean Squared Error (MSE):. Root Mean Squared Error (RMSE): Mean Absolute Error (MAER-squared (Coefficient of Determination): For Classification Accuracy: Precision: Recall: F1 score:

6. DIFFERENCE

7. The process of converting an acoustic signal , captured by a microphone or telephone ,to a set of words SPEECH RECOGNITION

8. How does it works

9. Types Speaker –dependent Speaker-independent

10. Thank you