The DOI matches its Crossref record.
An Algorithm for Determining the Endpoints of Isolated Utterances
Abstract
An important problem in speech processing is to detect the presence of speech in a background of noise. This problem is often referred to as the endpoint location problem. By accurately detecting the beginning and end of an utterance, the amount of processing of speech data can be kept to a minimum. The algorithm proposed for locating the endpoints of an utterance is based on two measures of the signal, zero crossing rate and energy. The algorithm is inherently capable of performing correctly in any reasonable acoustic environment in which the signal-to-noise ratio is on the order of 30 dB or better. The algorithm has been tested over a variety of recording conditions and for a large number of speakers and has been found to perform well across all tested conditions.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Speech recognition by machine: A review
Reddy, D. R. (1976) · Proceedings of the IEEE · doi:10.1109/proc.1976.10158 · Cited by 512
Introduction to Digital Speech Processing
Rabiner, L. R., & Schafer, R. W. (2007) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000001 · Cited by 319
Self-citationExperiments on neural net recognition of spoken and written text
Burr, D. J. (1988) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/29.1643 · Cited by 263
An experimental study of speech emotion recognition based on deep convolutional neural networks
Zheng, W. Q., Yu, J. S., & Zou, Y. X. (2015) · 2015 International Conference on Affective Computing and Intelligent Interaction (ACII) · doi:10.1109/acii.2015.7344669 · Cited by 187
Digital signal processing in studies of animal acoustical communication, including human speech
Davis, R. O. (1986) · Computer Methods and Programs in Biomedicine · doi:10.1016/0169-2607(86)90050-7 · Cited by 6
Metadata
- Authors
- Rabiner, L. R., & Sambur, M. R.
- Venue
- Bell System Technical Journal
- Published
- 1975-02
- Volume
- 54
- Issue
- 2
- Pages
- 297-315
- ISSN
- 0005-8580 (ISSN-L)
- OpenAlex
- W2032474878
- Cited by
- 558
- References
- 0
- FWCI
- 5.86
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: An Algorithm for Determining the Endpoints of Isolated Utterances. https://app.cochat.ai/papers/10.1002/j.1538-7305.1975.tb02840.x
- The paper
- Rabiner, L. R., & Sambur, M. R. (1975). An Algorithm for Determining the Endpoints of Isolated Utterances. Bell System Technical Journal. https://doi.org/10.1002/j.1538-7305.1975.tb02840.x
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.