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.

Extension and complexity reduction of TwinVQ audio coder

Abstract: This paper proposes two novel techniques for twinVQ (transform domain weighted interleave VQ) high-quality audio coding scheme for rates lower than 64 kbit/s. One is an extension of the weighted interleave technique to the time and input channel domains as well as the frequency domain. The other is an efficient representation scheme of the spectral envelope by means of a interpolated square root LPC (linear predictive coding) spectrum.

Analysis of speech signals of short pitch period by a sample-selective linear prediction

Abstract: The conventional linear prediction analysis has difficulties in estimating the vocal tract characteristics of voiced sounds uttered by females or children. This paper shows that the vocal tract characteristics of those speech signals can be estimated accurately by the sample-selective linear prediction (SSLP) method proposed by the authors. The SSLP is a two-stage linear prediction analysis employing only relevant sample values in the second stage analysis, while the conventional linear prediction method employs all the sample values with equal weights as predicted values. The accuracy of the proposed method in estimating formant frequencies is examined on synthetic vowels of short pitch periods. The validity of the method is confirmed by inspecting the estimated spectral envelopes and distributions of the estimated formant frequencies of natural vowels uttered by a female.

Method and system for low bit rate speech coding with speech recognition features and pitch providing reconstruction of the spectral envelope

Abstract: A method for encoding a digitized speech signal so as to generate data capable of being decoded as speech. A digitized speech signal is first converted to a series of feature vectors using for example known Mel-frequency Cepstral coefficients (MFCC) techniques. At successive instances instance of time a respective pitch value of the digitized speech signal is computed, and successive acoustic vectors each containing the respective pitch value and feature vector are compressed so as to derive therefrom a bit stream. A suitable decoder reverses the operation so as to extract the features vectors and pitch values, thus allowing speech reproduction and playback. In addition, speech recognition is possible using the decompressed feature vectors, with no impairment of the recognition accuracy and no computational overhead.

TD-PSOLA versus harmonic plus noise model in diphone based speech synthesis

Abstract: In an effort to select a speech representation for our next generation concatenative text-to-speech synthesizer, the use of two candidates is investigated; TD-PSOLA and the harmonic plus noise model, HNM. A formal listening test has been conducted and the two candidates have been rated regarding intelligibility, naturalness and pleasantness. Ability for database compression and computational load is also discussed. The results show that HNM consistently outperforms TD-PSOLA in all the above features except for computational load. HNM allows for high-quality speech synthesis without smoothing problems at the segmental boundaries and without buzziness or other oddities observed with TD-PSOLA.

Speech recognition by automated context creation

Abstract: A method for speech recognition can include generating a context-enhanced database from a system input. A voice-generated output can be generated from a speech signal by performing a speech recognition task to convert the speech signal into computer-processable segments. During the speech recognition task, the context-enhanced database can be accessed to improve the speech recognition rate. Accordingly, the speech signal can be interpreted with respect to words included within the context-enhanced database. Additionally, a user can edit or correct an output in order to generate the final voice-generated output which can be made available.