摘要
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.
原文 | English |
---|---|
頁(從 - 到) | 476-480 |
頁數 | 5 |
期刊 | Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH |
出版狀態 | Published - 9月 2014 |
事件 | 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, 新加坡 持續時間: 14 9月 2014 → 18 9月 2014 |