Showing papers on "Code-excited linear prediction published in 1976"

The factorial linear modelling: A Karhumen-Loeve approach to speech analysis

Abstract: This paper introduces a modified linear prediction method based on the Karhumen-Loeve expansion of the correlation matrix of the speech samples. This result is obtained via a new normalization of the parameters. It is shown that, due to some important properties of Toeplitz matrices, the poles of the A. R. model lie on the unit circle. Consequently, only the formant frequencies are computed and the result can be interpreted as a special discrete Fourier transform. Application to speech analysis is developped with a comparison to the usual linear prediction and ceptrum methods.

Linear predictive coding systems

Abstract: This paper describes the real time implementation of a Linear Predictive Coding algorithm that has been developed over the past five years. The algorithm chosen for the analyzer is a modification of the Covariance Method introduced by B. S. Atal [1],[2] of Bell Labs. The system for pitch extraction uses a minimum distance function correlation technique. A dynamic programming algorithm [3] is used for pitch smoothing and correction of isolated pitch errors. The synthesizer uses a transversal filter. Considerable time has been devoted to optimizing the running time and integer scaling of the different algorithms for real time implementation on a 16 bit mini-computer.

Word spotting in continuous speech using linear predictive coding

Abstract: Automatic speech recognition and understanding are currently receiving considerable attention.1 Most approaches to problems in these areas involve rather complicated systems. Typically, the acoustic waveform is first segmented into units such as phonemes or syl— lables. Semantic and syntactic information is then used to resolve ambiguities and to yield higher—order decisions. This complexity is probably necessary if the most general speech—recognition problems are to be solved.

An application of the linear prediction technique to efficient coding of speech segments

Abstract: Summary form only given, as follows. The paper deals with the application of linear prediction technique to the speech synthesis of both italian and german languages by Standard Speech Reproducing Units (SSRU), it is by combining elementary speech segments of standardized charac teristics extracted fron utterances of native speakers. The nain feature of the method presented is the possibility of synthesizing in a higly intelligible form any nessage of such languages with a very limited amount of data. So far the use of linear predictive coding of the previously realized SSRU sets allowed a memory occupation less than 16 kb for the synthesis of italian language and less than 32 k-bytes for the combined synthesis of italian and german languages. The data flow rate is about 1 kb/s. A key property of the code with respect to methods previously used (i.e. simple concatenation of original segments ) relies in the possibility of greatly enhancing the naturalness of the synthesized speech by varying pitch, amplitude and duration of the synthetic segments. Further, the quantitative rules obtained for generating the SSRU's are expected to be useful, at least as a preliminary investigation tool, for synthesis-by-rule.