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.

Improved linear predictive coding method for speech recognition

Abstract: In this paper, the improved linear predictive coding (LPC) coefficients of the frame are employed in the feature extraction method. In the proposed speech recognition system, the static LPC coefficients + dynamic LPC coefficients of the frame were employed as a basic feature. The framework of linear discriminant analysis (LDA) is used to derive an efficient and reduced-dimension speech parametric speech vector space for the speech recognition system. Using the continuous hidden Markov model (HMM) as the speech recognition model, the speech recognition system was successfully constructed. Experiments are performed on the isolated-word speech recognition task. It is found that the improved LPC feature extraction method is quite efficient.

A 13.0 kbit/s wideband speech codec based on SB-ACELP

Abstract: This paper describes a wideband (7 kHz) speech compression scheme operating at a bit rate of 13.0 kbit/s, i.e. 0.8 bit per sample. We apply a split-band (SB) technique, where the 0-6 kHz band is critically subsampled and coded by an ACELP approach. The high frequency signal components (6-7 kHz) are generated by an improved high-frequency-resynthesis (HFR) at the decoder such that no additional information has to be transmitted. In informal listening tests, the subjective speech quality was rated to be comparable to the CCITT G.722 wideband codec at 48 kbit/s.

Quality Assessment of Speech Enhancement Systems by Separation of Enhanced Speech, Noise, and Echo

Abstract: Quality assessment of speech enhancement systems is a non-trivial task, especially when (residual) noise and echo signalcomponents occur. We present a signal separation schemethat allows for a detailed analysis of unknown speech enhance-ment systems in a black box test scenario. Our approach sep-arates the speech, (residual) noise, and (residual) echo compo-nent of the speech enhancement system in the sending direc-tion (uplink direction). This makes it possible to independentlyjudge the speech degradation and the noise and echo attenua-tion/degradation. While state of the art tests always try to judgethe sending direction signal mixture, our new scheme allows amore reliable analysis in shorter time. It will be very usefulfor testing hands-free devices in practice as well as for testingspeech enhancement algorithms in research and development. Index Terms : objective signal quality assessment, non-blindsignal separation, speech enhancement, hands-free 1. Introduction In science, a comfortable way to evaluate speech enhancementalgorithms is to digitally add near-end speech and noise to theecho signal and thereby construct the microphone signal. Dur-ing the uplink processing of the speech enhancement (hands-free) system the operational influence on the noisy microphonesignal is then to be logged, and later applied individually to thespeech, echo, and noise components of the microphone signal(see, e.g., [1, 2, 3]). This presumes linear processing, as canbe found e.g. in frequency domain noise reduction, where again is applied to the spectral amplitudes. The strength of suchmethod is that one achieves three separate signals: The filteredspeechcomponent, thefilteredechocomponent, andthefilterednoise component, which represent the (slightly) distorted near-end talker’s speech signal, the suppressed echo signal, and theresidual noisesignal, respectively. Focusingonnoisereduction,e.g., aspects such as speech distortion, noise attenuation, andnoisedistortioncan thencomfortably bemeasured or auditivelyassessed.Thishowever isa

Near end listening enhancement optimized with respect to Speech Intelligibility Index

Abstract: Signal processing algorithms for near end listening enhancement allow to improve the intelligibility of clean (far end) speech for the near end listener who perceives not only the far end speech but also ambient background noise. A typical scenario is mobile communication conducted in the presence of acoustical background noise such as traffic or babble noise.

Noise suppression for speech processing based on machine-learning mask estimation

Abstract: Described are noise suppression techniques applicable to various systems including automatic speech processing systems in digital audio pre-processing. The noise suppression techniques utilize a machine-learning framework trained on cues pertaining to reference clean and noisy speech signals, and a corresponding synthetic noisy speech signal combining the clean and noisy speech signals. The machine-learning technique is further used to process audio signals in real time by extracting and analyzing cues pertaining to noisy speech to dynamically generate an appropriate gain mask, which may eliminate the noise components from the input audio signal. The audio signal pre-processed in such a manner may be applied to an automatic speech processing engine for corresponding interpretation or processing. The machine-learning technique may enable extraction of cues associated with clean automatic speech processing features, which may be used by the automatic speech processing engine for various automatic speech processing.