The aim of this study is to devise a computational method to predict cochlear implant (CI) speech recognition. Here, we describe a high-throughput screening system for optimizing CI speech processing strategies using hidden Markov model (HMM)-based automatic speech recognition (ASR). Word accuracy was computed on vocoded CI speech synthesized from primarily multi-channel temporal envelope information. The ASR performance increased with the number of channels in a similar manner displayed in human recognition scores. Results showed the computational method of HMM-based ASR offers better process control for comparing signal carrier type. Training-test mismatch reduction provided a novel platform for reevaluating the relative contributions of spectral and temporal cues to human speech recognition.
Bibliographic reference. Lin, Payton / Chen, Fei / Wang, Syu Siang / Lai, Ying-Hui / Tsao, Yu (2014): "Automatic speech recognition with primarily temporal envelope information", In INTERSPEECH-2014, 476-480.