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.

Autoregressive models with time-dependent log area ratios

Abstract: A large class of stationary signals, containing speech signals, but not restricted to them, can be represented by time-varying models, the coefficients of which are finite linear combinations of known time functions. Such models have been found useful for speech recognition and speech synthesis, but they suffer in this last application from a lack of stability. A time-varying area-ratio (AR) model, into which the time-dependency is coded through log-area ratios to ensure stability is described. Two algorithms for the estimation of these time-varying log area ratios are proposed; the first one is an approximation using a lattice filter, while the second one minimizes a least-squares criterion. The evaluation of their performance is obtained by a set of simulations. An example of speech signal modeled with these time-varying log area ratios shows the usefulness of this approach for speech synthesis and recognition. >

Speech quality indication

Abstract: A voice communications device (4) and speech processing method are described. A speech signal, generated by a microphone (41) in response to speech input (2) in to the microphone (41) by a user (1), has a proportion extracted therefrom. The speech signal is transmitted to an appliction apparatus that may be integral or remote. A speech quality value is evaluated for the extracted speech signal. An indication of the quality of the speech signal, based on the speech quality value, is indicated to the user. Thus a direct indication of the current quality of a received speech signal, in a form that is easily interpreted by a non-expert user of the device, is provided, thereby providing the use with an opportunity to improve the sppech quality. Examples of appliction apparatus include hands-free telephones and automatic speech recognition systems.

Speech and music classification in audio documents

Abstract: To index efficiently the soundtrack of multimedia documents, it is necessary to extract elementary and homogeneous acoustic segments. In this paper, we explore such a prior partitioning which consists in detect the two basic components, which are speech and music components. The originality of this work is that music and speech are not considered as two classes and two classification systems are independently defined, a speech/non-speech one and a music/non-music one. This approach permits to better characterize and discriminate each component: in particular, two different feature spaces are necessary as two pairs of Gaussian mixture models. More, the acoustic signal is divided into four types of segments: speech, music, speech-music and other. The experiments are performed on the soundtracks of audio video documents (films, TV sport broadcasts). The performance proves the interest of this approach, so called the Differentiated Modeling Approach.

Multi-segment vector quantizer for a speech coder suitable for use in a radiotelephone

Abstract: A Vector-Sum Excited Linear Predictive Coding (VSELP) speech coder provides improved quality and reduced complexity over a typical speech coder. VSELP uses a codebook which has a predefined structure such that the computations required for the codebook search process can be significantly reduced. This VSELP speech coder uses single or multi-segment vector quantizer of the reflection coefficients based on a Fixed-Point-Lattice-Technique (FLAT). Additionally, this speech coder uses a pre-quantizer to reduce the vector codebook search complexity and a high-resolution scalar quantizer to reduce the amount of memory needed to store the reflection coefficient vector codebooks. Resulting in a high quality speech coder with reduced computations and storage requirements.