Automatic speech recognition with primarily temporal envelope information

Payton Lin*, Fei Chen, Syu Siang Wang, Ying Hui Lai, Yu Tsao

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


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.


  • Cochlear implant
  • HMM-based ASR
  • Vocoder


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