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.

A family of distortion measures based upon projection operation for robust speech recognition

Abstract: Consideration is given to the formulation of speech similarity measures, a fundamental component in recognizer designs, that are robust to the change of ambient conditions. The authors focus on the speech cepstrum derived from linear prediction coefficients (the LPC cepstrum). By using some common models for noisy speech, they show analytically that additive white noise reduces the norm (length) of the LPC cepstral vectors. Empirical observations on the parameter histograms not only confirm the analytical results through the use of noise models but further reveal that at a given (global) signal-to-noise ratio (SNR), the norm reduction on cepstral vectors with larger norms is generally less than on vectors with smaller norms, and that lower order coefficients are more affected than higher order terms. In addition, it is found that the orientation (or direction) of the cepstral vector is less susceptible to noise perturbation than the vector norm. As a consequence of the above results, a family of distortion measures based on the projection between two cepstral vectors is proposed. The new measures have the same computational efficiency as the band-pass cepstral distortion measure. >

Speech recognition experiments with linear predication, bandpass filtering, and dynamic programming

Abstract: Automatic speech recognition experiments are described in which several popular preprocessing and classification strategies are compared. Preprocessing is done either by linear predictive analysis or by bandpass filtering. The two approaches are shown to produce similar recognition scores. The classifier uses either linear time stretching or dynamic programming to achieve time alignment. It is shown that dynamic programming is of major importance for recognition of polysyllabic words. The speech is compressed into a quasi-phoneme character string or preserved uncompressed. Best results are obtained with uncompressed data, using nonlinear time registration for multisyllabic words.

Methods and apparatus for non-acoustic speech characterization and recognition

Abstract: By simultaneously recording EM wave reflections (21) and acoustic speech information (24), the positions and velocities of the speech organs (2, 3, 4) as speech is articulated can be defined for each acoustic speech unit (20). Well defined time frames and feature vectors (6, 7, 8, 9) describing the speech, to the degree required, can be formed. Such feature vectors (6, 7, 8, 9) can uniquely characterize the speech unit (20) being articulated each time frame. The onset of speech, rejection of external noise, vocalized pitch periods, articulator conditions, accurate timing, the identification of the speaker, acoustic speech unit (20) recognition, and organ mechanical parameters can be determined.

Quality improvement of LPC-processed noisy speech by using spectral subtraction

Abstract: Numerous noise-suppression techniques have been developed for operating at the front end of low-bit-rate digital voice terminals. Some of these techniques have been evaluated by standardized intelligibility tests such as the diagnostic rhyme test (DRT). It is well known that the use of a noise suppressor seldom improves the DRT score even though listeners have had the impression that speech quality was enhanced. Unfortunately, noise suppressors have only occasionally been evaluated by standardized quality tests. The authors supplement quality test data for reference purposes. They use the diagnostic acceptability measure (DAM) to evaluate speech quality of the latest 2400-b/s linear-predictive coder (LPC) with a noise suppressor at the front end. They used a spectral subtraction technique for noise suppression. Ten different sets of noisy speech recorded at actual military platforms (such as a helicopter, tank, turboprop, helicopter carrier, or jeep) were input sources. The magnitude of the DAM improvement is substantial: as much as six points on the average, which is large enough to upgrade speech quality somewhat. >

Improving performance of multi-pulse LPC coders at low bit rates

Abstract: The multi-pulse excitation model provides a method for producing natural-sounding speech at medium to low bit rates. Multi-pulse analysis obtains the all-pole filter excitation by minimizing a spectrally-weighted mean-squared error between the original and synthetic speech signals. Although the method provides high quality speech around 10 kbits/sec, speech quality suffers if the bit rate is lowered. In this paper, we focus on problems encountered in attempting to maintain speech quality while synthesizing speech using multi-pulse excitation at lower bit rates.