The number of spectral channels required for speech recognition depends on the difficulty of the listening situation.

Cochlear implants provide a limited number of electrodes, each of which represents a channel of spectral information. Studies have shown that implant recipients are not receiving all of the information from the channels presented to their implant. The present paper provides a quantitative framework for evaluating how many spectral channels of information are necessary for speech recognition. Speech and melody recognition data from previous studies with cochlear implant simulations are compared as a function of the number of spectral channels of information. A quantitative model is applied to the results. Speech recognition performance increases as the number of spectral channels increases. A sigmoid function best describes this increase when plotted as a function of the log number of channels. As speech materials become more difficult, the function shifts to the right, indicating that more spectral channels of information are required. A model proposed by Plomp provides a single index to relate the difficulty of the task to the number of spectral channels needed for moderate recognition performance. In conclusion, simple sentence recognition in quiet can be achieved with only 3-4 channels of spectral information, while more complex materials can require 30 or more channels for an equivalent level of performance. The proposed model provides a single index that not only quantifies the number of functional channels in a cochlear implant, but also predicts the level of performance for different listening tasks.