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Linear predictive coding

About: Linear predictive coding is a research topic. Over the lifetime, 6565 publications have been published within this topic receiving 142991 citations. The topic is also known as: Linear predictive coding, LPC.


Papers
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Patent
16 Feb 2007
TL;DR: In this article, a method for varying speech speed is presented, which includes the following steps: receive an original speech signal, calculate a pitch period of the original signal, define search ranges according to pitch period, find a maximum within each of the search ranges, divide the signal into speech sections according to the maxima, and obtain a speed-varied speech signal by applying a speed varying algorithm to each speech section of the signal according to a speed changing command.
Abstract: A method for varying speech speed is provided. The method includes the following steps: receive an original speech signal; calculate a pitch period of the original speech signal; define search ranges according to the pitch period; find a maximum within each of the search ranges of the original speech signal; divide the original speech signal into speech sections according to the maxima; obtain a speed-varied speech signal by applying a speed-varying algorithm to each speech section of the original speed signal according to a speed-varying command; and eventually, output the speed-varied speech signal.

28 citations

Journal ArticleDOI
TL;DR: The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding.
Abstract: In this paper, we propose to employ predictive coding for lossy compression of synthetic aperture radar (SAR) raw data. We exploit the known result that a blockwise normalized SAR raw signal is a Gaussian stationary process in order to design an optimal decorrelator for this signal. We show that, due to the statistical properties of the SAR signal, an along-range linear predictor with few taps is able to effectively capture most of the raw signal correlation. The proposed predictive coding algorithm, which performs quantization of the prediction error, optionally followed by entropy coding, exhibits a number of advantages, and notably an interesting performance/complexity trade-off, with respect to other techniques such as flexible block adaptive quantization (FBAQ) or methods based on transform-coding; fractional output bit-rates can also be achieved in the entropy-constrained mode. Simulation results on real-world SIR-C/X-SAR as well as simulated raw and image data show that the proposed algorithm outperforms FBAQ as to SNR, at a computational cost compatible with modern SAR systems.

28 citations

Proceedings ArticleDOI
04 May 2014
TL;DR: Several strategies involving front-end filter bank redistribution, cepstral dimensionality reduction, and lexicon expansion for alternative pronunciations are proposed to improve robustness of automatic speech recognition of whispered speech with neutral-trained acoustic models.
Abstract: This study focuses on acoustic variations in speech introduced by whispering, and proposes several strategies to improve robustness of automatic speech recognition of whispered speech with neutral-trained acoustic models. In the analysis part, differences in neutral and whispered speech captured in the UT-Vocal Effort II corpus are studied in terms of energy, spectral slope, and formant center frequency and bandwidth distributions in silence, voiced, and unvoiced speech signal segments. In the part dedicated to speech recognition, several strategies involving front-end filter bank redistribution, cepstral dimensionality reduction, and lexicon expansion for alternative pronunciations are proposed. The proposed neutral-trained system employing redistributed filter bank and reduced features provides a 7.7% absolute WER reduction over the baseline system trained on neutral speech, and a 1.3% reduction over a baseline system with whisper-adapted acoustic models.

28 citations

PatentDOI
Milind Mahajan1, Yonggang Deng1, Alejandro Acero1, Asela Gunawardana1, Ciprian Chelba1 
TL;DR: In this article, a method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units, which is used with the sequence of actual speech units to form a confusion model.
Abstract: A method of modeling a speech recognition system includes decoding a speech signal produced from a training text to produce a sequence of predicted speech units. The training text comprises a sequence of actual speech units that is used with the sequence of predicted speech units to form a confusion model. In further embodiments, the confusion model is used to decode a text to identify an error rate that would be expected if the speech recognition system decoded speech based on the text.

28 citations

Patent
Tadashi Suzuki1
17 Oct 1996
TL;DR: In this paper, an estimated SN(Signal Noise)-ratio is calculated for a time-series feature vector of noise-superimposed speech by using a noise-free speech model and a noise model.
Abstract: An estimated-SN(Signal Noise)-ratio is calculated for a time-series feature vector of noise-superimposed speech by using a noise-free speech model and a noise model. A noise-superimposed model is generated based on the estimated-SN-ratio. A likelihood between the time-series feature vector of noise-superimposed speech and the noise-superimposed model is calculated to obtain likelihood information. A noise spectrum included in the noise-superimposed speech is estimated from the likelihood information.

28 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20239
202225
202126
202042
201925
201837