Automatic Sentence Modality Recognition in Children's Speech, and Its Usage Potential in the Speech Therapy.
Dávid Sztahó, Katalin Nagy, Klára Vicsi · 2008
In the Laboratory of Speech Acoustics prosody recognition experiments have been prepared, in which, among the others, we were searching for the possibilities of the recognition of sentence modalities. Due to our promising results in the sentence modality recognition, we adopted the method for children modality recognition, and looked for the possibility, how it can be used as an automatic feedback in an audio - visual pronunciation teaching and training system. Our goal was to develop a sentence intonation teaching and training system for speech handicapped children, helping them to learn the correct prosodic pronunciation of sentences. In the experiment basic sentence modality models have been developed and used. For the training of these models, we have recorded a speech prosody database with correctly speaking children, processed and segmented according to the types of modalities. At the recording of this database, 59 children read a text of one word sentences, simple and complex sentences. HMM models of modality types were built by training the recognizer with this correctly speaking children database. The result of the children sentence modality recognition was not adequate enough for the purpose of automatic feedback in case of pronunciation training. Thus another way of classification was prepared. This time the recordings of the children were sorted rigorously by the type of the intonation curves of sentences, which were different in many cases from the sentence modality classes. With the new classes, further tests were carried out. The trained HMM models were used, not for the recognition of the modality of sentences, but checking the correctness of the intonation of sentences pronounced by speech handicapped children. Therefore, an initial database, consisting of the recordings of the voices of two speech handicapped children had been prepared, similar to the database of healthy children.