Automatic speech recognition with primarily temporal envelope information

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

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

Department of Biomedical Engineering

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	476-480
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
State	Published - Sep 2014
Event	15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 - Singapore, Singapore Duration: 14 Sep 2014 → 18 Sep 2014

Keywords

Cochlear implant
HMM-based ASR
Vocoder

Cite this

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title = "Automatic speech recognition with primarily temporal envelope information",

abstract = "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.",

keywords = "Cochlear implant, HMM-based ASR, Vocoder",

author = "Payton Lin and Fei Chen and Wang, {Syu Siang} and Lai, {Ying Hui} and Yu Tsao",

note = "Publisher Copyright: Copyright {\textcopyright} 2014 ISCA.; 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014 ; Conference date: 14-09-2014 Through 18-09-2014",

year = "2014",

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language = "English",

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journal = "Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH",

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TY - JOUR

T1 - Automatic speech recognition with primarily temporal envelope information

AU - Lin, Payton

AU - Chen, Fei

AU - Wang, Syu Siang

AU - Lai, Ying Hui

AU - Tsao, Yu

PY - 2014/9

Y1 - 2014/9

N2 - 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.

AB - 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.

KW - Cochlear implant

KW - HMM-based ASR

KW - Vocoder

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M3 - Conference article

AN - SCOPUS:84910029520

SN - 2308-457X

SP - 476

EP - 480

JO - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

JF - Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

T2 - 15th Annual Conference of the International Speech Communication Association: Celebrating the Diversity of Spoken Languages, INTERSPEECH 2014

Y2 - 14 September 2014 through 18 September 2014

ER -

Automatic speech recognition with primarily temporal envelope information

Abstract

Keywords

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