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.

A fast and reliable rate of speech detector

Abstract: In this paper, we present a new rate-of-speech (ROS) detector that operates independently from the recognition process. This detector is evaluated on the TIMIT corpus and positioned with respect to other ROS detectors. The ROS estimate is subsequently used to compensate for the effects of unusual speech rates on continuous speech recognition. We report on results obtained with two ROS compensation techniques on a speaker-independent acoustic-phonetic decoding task.

System for predictive coding/decoding of a digital speech signal by embedded-code adaptive transform

Abstract: A system for predictive coding of a digital speech signal with embedded codes used in any transmission system or for storing speech signals. The coded digital signal (Sn) is formed by a coded speech signal and, if appropriate, by auxiliary data. A perceptual weighting filter is formed by a filter for short-term prediction of the speech signal to be coded, in order to produce a frequency distribution of the quantization noise. A circuit makes it possible to perform the subtraction from the perceptual signal of the contribution of the past excitation signal P0 n to deliver an updated perceptual signal Pn. A long-term prediction circuit is formed, as a closed loop, from a dictionary updated by the modelled page excitation r1 n for the lowest throughput and makes it possible to deliver an optimal waveform and an associated estimated gain which make up the estimated perceptual signal P1 n. An orthonormal transform module includes an adaptive transform module and a module for progressive modelling by orthogonal vectors, thus making it possible to deliver indices representing the coded speech signal. A circuit makes it possible to insert auxiliary data by stealing bits from the coded speech signal. Decoding is performed through extraction of datasignal and transmission of indices representing coded speech signal which is modelled at the minimum throughput.

Mixed speech recognition

Abstract: The claimed subject matter includes a system and method for recognizing mixed speech from a source. The method includes training a first neural network to recognize the speech signal spoken by the speaker with a higher level of a speech characteristic from a mixed speech sample. The method also includes training a second neural network to recognize the speech signal spoken by the speaker with a lower level of the speech characteristic from the mixed speech sample. Additionally, the method includes decoding the mixed speech sample with the first neural network and the second neural network by optimizing the joint likelihood of observing the two speech signals considering the probability that a specific frame is a switching point of the speech characteristic.

SVM-based audio scene classification

Abstract: Audio scene classification is very important in audio indexing, retrieval and video content analysis. In this paper we present our approach that uses support vector machine (SVM) for audio scene classification, which classifies audio clips into one of five classes: pure speech, non-pure speech, music, environment sound, and silence. Among them, non-pure speech may further be divided into speech with music and speech with noise. We also describe two methods to select effective and robust audio feature sets. Based on these feature sets, we have evaluated and compared the performance of two kinds of classification frameworks on a testing database that is composed of about 4-hour audio data. The experimental results have shown that the SVM-based method yields high accuracy with high processing speed.