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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
Kenneth Jong1
TL;DR: In this article, a speech-text-transmit communication over data networks includes speech recognition devices and text to speech conversion devices that translate speech signals input to the terminal into text and text data received from a data network into speech output signals.
Abstract: An apparatus and method for speech-text-transmit communication over data networks includes speech recognition devices and text to speech conversion devices that translate speech signals input to the terminal into text and text data received from a data network into speech output signals. The speech input signals are translated into text based on phonemes obtained from a spectral analysis of the speech input signals. The text data is transmitted to a receiving party over the data network as a plurality of text data packets such that a continuous stream of text data is obtained. The receiving party's terminal receives the text data and may immediately display the text data and/or translate it into speech output signals using the text to speech conversion device. The text to speech conversion device uses speech pattern data stored in a speech pattern database for synthesizing a human voice for playing of the speech output signals using a speech output device.

83 citations

Proceedings ArticleDOI
23 May 1989
TL;DR: A phonetically based segmentation of speech is performed to classify segments into five classes: onset, unvoiced low-pass voiced, steady-state voiced, Steady- state voiced, and transient voiced, using a distinctive coding scheme based on vector excitation coding (VXC).
Abstract: A phonetically based segmentation of speech is performed to classify segments into five classes: onset, unvoiced low-pass voiced, steady-state voiced, and transient voiced. The segment lengths are constrained to an integer multiple of a unit-frame. For each segment class, a distinctive coding scheme based on vector excitation coding (VXC) is used. The maximum bit-rate is 3.6 kb/s, and a moderate coding delay of 45 ms is incurred. Performance is roughly comparable to conventional VXC/CELP (code-excited linear prediction) coding at 4.8 kb/s. >

82 citations

Proceedings ArticleDOI
19 Apr 1994
TL;DR: The technique, called nonlinear predictive coding, is shown to be superior to the LPC technique and two different nonlinear predictors are presented, one based on a second-order Volterra filter, and the other on a time delay neural network, which is found to be the more suitable for speech coding applications.
Abstract: Addresses the question of how to extract the nonlinearities in speech with the prime purpose of facilitating coding of the residual signal in residual excited coders. The short-term prediction of speech in speech coders is extensively based on linear models, e.g. the linear predictive coding technique (LPC), which is one of the most basic elements in modern speech coders. This technique does not allow extraction of nonlinear dependencies. If nonlinearities are absent from speech the technique is sufficient, but if the speech contains nonlinearities the technique is inadequate. The authors give evidence for nonlinearities in speech and propose nonlinear short-term predictors that can substitute the LPC technique. The technique, called nonlinear predictive coding, is shown to be superior to the LPC technique. Two different nonlinear predictors are presented. The first is based on a second-order Volterra filter, and the second is based on a time delay neural network. The latter is shown to be the more suitable for speech coding applications. >

82 citations

Proceedings ArticleDOI
21 Apr 1997
TL;DR: This paper focuses on the improvements on prosody and acoustic modeling, which are all derived through the use of probabilistic learning methods in Whisper TTS engine.
Abstract: The Whistler text-to-speech engine was designed so that we can automatically construct the model parameters from training data. This paper focuses on the improvements on prosody and acoustic modeling, which are all derived through the use of probabilistic learning methods. Whistler can produce synthetic speech that sounds very natural and resembles the acoustic and prosodic characteristics of the original speaker. The underlying technologies used in Whistler can significantly facilitate the process of creating generic TTS systems for a new language, a new voice, or a new speech style. Whisper TTS engine supports Microsoft Speech API and requires less than 3 MB of working memory.

82 citations

Journal ArticleDOI
TL;DR: Experiments show large intelligibility improvements with the proposed method over the unprocessed noisy speech and better performance than one state-of-the art method.
Abstract: In this letter the focus is on linear filtering of speech before degradation due to additive background noise. The goal is to design the filter such that the speech intelligibility index (SII) is maximized when the speech is played back in a known noisy environment. Moreover, a power constraint is taken into account to prevent uncomfortable playback levels and deal with loudspeaker constraints. Previous methods use linear approximations of the SII in order to find a closed-form solution. However, as we show, these linear approximations introduce errors in low SNR regions and are therefore suboptimal. In this work we propose a nonlinear approximation of the SII which is accurate for all SNRs. Experiments show large intelligibility improvements with the proposed method over the unprocessed noisy speech and better performance than one state-of-the art method.

82 citations


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