Speech Impairment Using Hybrid Model of Machine Learning

Speech impairment is a technique in which speech sound signals are produced that is effective to communicate with others. Speech impairments can be of any type, from mild, where one occasionally has trouble producing a couple of words, to severe, where one is not being capable of producing speech sound signals at all. The basic outcome is to study the hybrid model of machine learning for speech impairment. For effective machine learning results, it uses a specific model, effective techniques, knowledge parameters and advanced trees algorithm for displaying the valuable results. We rely on speech as one of the primary methods of communicating with others. Speech impairments in childhood can have a negative influence on social development. Speech impairments at all stages of life can lead to embarrassment and shame. The result of learning patterns on various human affliction diagnoses supports medical specialists established on the effects of starting, even though some results exhibit the same factors. In this paper, Parkinson's dataset from the UCI library is used with the top four speech-related parameters and obtains a higher accuracy level with a hybrid model compared with the other classifiers.