Vector quantization of speech frames based on self-organizing maps.
We propose a speech compression technique based on vector quantization. A neural network with unsupervised learning is used to implement the vector quantizer. Some basic aspects related to speech signal processing are presented, as well as some general issues concerning the vector quantization problem. The idea of using a codebook to perform speech compression is introduced, and the use of a 2-dimensional self-organizing Kohonen map to generate the codebook is proposed. Simulation results are presented, giving some insights on the network topology, its initialization and training strategies, and codebook size. Finally, a comparison of speech quality obtained with our method and with a well-known compression algorithm is made.
Simões
Flávio Olmos
FO
Telecommunications Research Center (CPqD), Rod. Campinas-Mogi-Mirim (SP 340), km 118,5, Campinas, SP, 13086-902, Brazil. simoes@cpqd.com.br
Neto
Mário Uliani
MU
Machado
Jeremias Barbosa
JB
Nagle
Edson José
EJ
Runstein
Fernando Oscar
FO
de Campos Teixeira Gomes
Leandro
L
D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013060:Speech / Q000502:physiology*; D013067:Speech Perception / Q000502:physiology