phonetic classification and recognition
Phonetic Classification and Recognition Using the Multi-Layer Perceptron
In this paper, we will describe several extensions to our earlier work, utiliz(cid:173) ing a segment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed within a set of experiments that attempts to recognize 38 vowels and consonants in American English independent of speaker. When evaluated on the TIMIT database, our system achieves an accuracy of 56%.
Phonetic Classification and Recognition Using the Multi-Layer Perceptron
Leung, Hong C., Glass, James R., Phillips, Michael S., Zue, Victor W.
In this paper, we will describe several extensions to our earlier work, utilizing a segment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed within a set of experiments that attempts to recognize 38 vowels and consonants in American English independent of speaker.
Phonetic Classification and Recognition Using the Multi-Layer Perceptron
Leung, Hong C., Glass, James R., Phillips, Michael S., Zue, Victor W.
In this paper, we will describe several extensions to our earlier work, utilizing a segment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed within a set of experiments that attempts to recognize 38 vowels and consonants in American English independent of speaker.
Phonetic Classification and Recognition Using the Multi-Layer Perceptron
Leung, Hong C., Glass, James R., Phillips, Michael S., Zue, Victor W.
In this paper, we will describe several extensions to our earlier work, utilizing asegment-based approach. We will formulate our segmental framework and report our study on the use of multi-layer perceptrons for detection and classification of phonemes. We will also examine the outputs of the network, and compare the network performance with other classifiers. Our investigation is performed within a set of experiments that attempts to recognize 38 vowels and consonants in American English independent of speaker.