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.

Digital speech processing system having reduced encoding bit requirements

Abstract: A digitized speech signal is divided into sections and each section is analyzed by the linear prediction method to determine the coefficients of a sound formation model, a sound volume parameter, information concerning voiced or unvoiced excitation and the period of the vocal band base frequency. In order to improve the quality of speech without increasing the data rate, redundance reducing coding of the speech parameters is effected. The coding of the speech parameters is performed in blocks of two or three adjacent speech sections. The parameters of the first speech section are coded in a complete form, and those of the other speech sections in a differential form or in part not at all. The average number of bits required per speech section is reduced to compensate for the increased section rate, so that the overall data rate is not increased.

Comparison of DTW and HMM for isolated word recognition

Abstract: This study proposes limited vocabulary isolated word recognition using Linear Predictive Coding(LPC) and Mel Frequency Cepstral Coefficients(MFCC) for feature extraction, Dynamic Time Warping(DTW) and discrete Hidden Markov Model (HMM) for recognition and their comparisons. Feature extraction is carried over the speech frame of 300 samples with 100 samples overlap at 8 KHz sampling rate of the input speech. MFCC analysis provides better recognition rate than LPC as it operates on a logarithmic scale which resembles human auditory system whereas LPC has uniform resolution over the frequency plane. This is followed by pattern recognition. Since the voice signal tends to have different temporal rate, DTW is one of the methods that provide non-linear alignment between two voice signals. Another method called HMM that statistically models the words is also presented. Experimentally it is observed that recognition accuracy is better for HMM compared with DTW. The database used is TI-46 isolated word corpus zero-nine from Linguist Data Consortium.

Bias propagation in the autocorrelation method of linear prediction

Abstract: Many low bit-rate speech coders use the autocorrelation method (ACM) to find a linear prediction model of the speech signal. A time-domain analysis of the ACM for autoregressive estimation is given. It is shown that a small bias in a reflection coefficient close to one in absolute value is propagated and prohibits an accurate estimation of further reflection coefficients. Tapered data windows largely reduce this effect, but increase the variance of the models.

Digital audio watermarking algorithm based on linear predictive coding in wavelet domain

Abstract: A digital audio watermarking algorithm based on discrete wavelet transform is presented. A visually significant binary image via some pre-processing and SS modulating is embedded in audio low-middle frequency coefficients in wavelet domain. A scheme of watermark detection is presented by using linear predictive coding, and it does not use the original signal during extracting watermark. The BER is improved 10%-15% in this algorithm compared with the algorithm in Wang R.D. and Chai P.Q. (2003). Experimental results show that the watermark is imperceptible and the algorithm is robust to many attacks, such as low pass filtering, resampling, MP3 compression and so on.

Non-linear frequency scale mapping for voice conversion in text-to-speech system with cepstral description

Abstract: Voice conversion, i.e. modification of a speech signal to sound as if spoken by a different speaker, finds its use in speech synthesis with a new voice without necessity of a new database. This paper introduces two new simple non-linear methods of frequency scale mapping for transformation of voice characteristics between male and female or childish. The frequency scale mapping methods were developed primarily for use in the Czech and Slovak text-to-speech (TTS) system designed for the blind and based on the Pocket PC device platform. It uses cepstral description of the diphone speech inventory of the male speaker using the source-filter speech model or the harmonic speech model. Three new diphone speech inventories corresponding to female, childish and young male voices are created from the original male speech inventory. Listening tests are used for evaluation of voice transformation and quality of synthetic speech.