15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Automatic Speech Recognition with Primarily Temporal Envelope Information

Payton Lin (1), Fei Chen (2), Syu Siang Wang (1), Ying-Hui Lai (1), Yu Tsao (1)

(1) Academia Sinica, Taiwan
(2) University of Hong Kong, China

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.

Full Paper

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.