The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Digital inverse filtering-a new tool for formant trajectory estimation
Abstract
A new algorithm, based upon a digital inverse filter formulation, is presented and shown to be quite useful for estimating resonance or formant structure of voiced speech. The output of the algorithm is a set of raw data corresponding to peak frequencies versus time which is then used to estimate the first three and sometimes four continuously varying formant trajectories. Although an algorithm for automatically extracting the formants from the raw data is not presented here, for nearly 90 percent of the time an automatic decision algorithm is trivial, namely, the first three peaks of the reciprocal of the inverse filter spectrum define the first three formants.
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Citations
Cites
Control Methods Used in a Study of the Vowels
Peterson, G. E., & Barney, H. L. (1952) · The Journal of the Acoustical Society of America · doi:10.1121/1.1906875 · Cited by 2,837 · cited 2×
“From the Peterson-Barney study [ 17 ] it can be seen that the first three formants for vowels in a CVC environment are generally below 3 kHz.”
Background
Metadata
- Authors
- Markel, J.
- Venue
- IEEE Transactions on Audio and Electroacoustics
- Published
- 1972-06
- Volume
- 20
- Issue
- 2
- Pages
- 129-137
- ISSN
- 0018-9278 (ISSN-L)
- OpenAlex
- W1979871770
- Cited by
- 165
- References
- 1
- FWCI
- 12.76
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Digital inverse filtering-a new tool for formant trajectory estimation. https://app.cochat.ai/papers/10.1109/tau.1972.1162367
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- Markel, J. (1972). Digital inverse filtering-a new tool for formant trajectory estimation. IEEE Transactions on Audio and Electroacoustics. https://doi.org/10.1109/tau.1972.1162367
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
