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.

The spectral envelope estimation vocoder

Abstract: This paper describes a low bit-rate vocoder designed for improved speech reproduction quality and robustness. The vocoder is designed around a new algorithm, the spectral envelope estimator, which forms the nucleus of the spectral analyzer. In addition to estimating the speech spectrum, the spectral analyzer also allows determination of a continuous estimate of the background noise spectrum, which is used for noise suppression. A maximum-likelihood pitch estimator, which shares the signal processing of the spectral envelope estimator, has been integrated into the vocoder to yield accurate pitch estimates of noisy speech. This system is capable of good quality speech reproduction at bit rates down to 2.4 kbits/s.

Speech analysis and synthesis system

Abstract: A speech analysis and synthesis system operates to determine a sound source signal for the entire interval of each speech unit which is to be used for speech synthesis, according to a spectrum parameter obtained from each speech unit based on cepstrum. The sound source signal and the spectrum parameter are stored for each speech unit. Speech is synthesized according to the spectrum parameter while controlling prosody of the sound source signal. The spectrum of the synthesized speech is compensated through filtering based on cepstrum.

Complexity reduction methods for vector excitation coding

Abstract: Vector Excitation Coding (VXC) is based on a new and general source-filter modeling technique in which the excitation signal for a speech production model is encoded at very low bit-rates using vector quantization. Various speech coder structures which fall into this class have recently been shown to reproduce speech with very high perceptual quality. The primary drawback of VXC is the large amount of computation required in the process of selecting an optimal excitation signal. We present several schemes in this paper which substantially reduce search computation in VXC coders while retaining their remarkably high reconstructed speech quality.

Text-Independent Voice Conversion Based on Unit Selection

Abstract: So far, most of the voice conversion training procedures are text-dependent, i.e., they are based on parallel training utterances of source and target speaker. Since several applications (e.g. speech-to-speech translation or dubbing) require text-independent training, over the last two years, training techniques that use non-parallel data were proposed. In this paper, we present a new approach that applies unit selection to find corresponding time frames in source and target speech. By means of a subjective experiment it is shown that this technique achieves the same performance as the conventional text-dependent training.

Voiced-unvoiced-silence detection using the Itakura LPC distance measure

Abstract: One of the most difficult problems in speech analysis is reliable discrimination among silence, unvoiced speech, and voiced speech which has been transmitted over a telephone line. Although several methods have been proposed for making this 3-level decision, these schemes have met with only modest success. In this paper a novel approach to the voiced-unvoiced-silence detection problem is proposed in which a spectral characterization of each of the 3 classes of signal is obtained during a training session, and an LPC distance metric and an energy distance are nonlinearly combined to make the final discrimination. This algorithm has been tested over conventional switched telephone lines, across a variety of speakers, and has been found to have an error rate of about 5%, with the majority of the errors (about 2/3) occurring at the boundaries between signal classes. The algorithm is currently being used in a speaker independent word recognition system.