Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
Abstract
Several parametric representations of the acoustic signal were compared with regard to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary included many phonetically similar monosyllabic words, therefore the emphasis was on the ability to retain phonetically significant acoustic information in the face of syntactic and duration variations. For each parameter set (based on a mel-frequency cepstrum, a linear frequency cepstrum, a linear prediction cepstrum, a linear prediction spectrum, or a set of reflection coefficients), word templates were generated using an efficient dynamic warping method, and test data were time registered with the templates. A set of ten mel-frequency cepstrum coefficients computed every 6.4 ms resulted in the best performance, namely 96.5 percent and 95.0 percent recognition with each of two speakers. The superior performance of the mel-frequency cepstrum coefficients may be attributed to the fact that they better represent the perceptually relevant aspects of the short-term speech spectrum.
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Many selected features were robust to transcription quality and may even exploit poor ASR performance to identify participants with dementia.
“Additionally, many of the features they selected showed robustness to transcription quality, possibly even taking advantage of the poor ASR performance to identify participants with dementia.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [48]
Even with a relatively high WER, the WER itself was a reliable feature for classifying different types of subjects.
“The off-the-shelf ASR model used in this work had a relatively high WER, but they were interestingly able to show that the WER itself was a reliable feature for classifying the different types of subjects.”
From A Review of Automated Speech and Language Features for Assessment of Cognitive and Thought Disorders (2020) · cites this paper as [48]
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- Authors
- Davis, S., & Mermelstein, P.
- Affiliations
- Haskins Laboratories (US)
- ORCID
- Mermelstein, P.
- Venue
- IEEE Transactions on Acoustics, Speech, and Signal Processing
- Published
- 1980-08
- Volume
- 28
- Issue
- 4
- Pages
- 357-366
- ISSN
- 0096-3518 (ISSN-L)
- OpenAlex
- W2148154194
- Cited by
- 5,465
- References
- 20
- Citations by year
- 2026: 279 · 2025: 271 · 2024: 217 · 2023: 231 · 2022: 231 · 2021: 284 · 2020: 254 · 2019: 270 · 2018: 231 · 2017: 239 · 2016: 226 · 2015: 249 · 2014: 276 · 2013: 209 · 2012: 214
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Acoustics Speech and Signal Processing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Phonetics and Phonology Research
- Keywords
- monosyllabic word recognition, continuous speech recognition, reflection coefficient, time registration, syntactic variation
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