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.

Speech mode based multi-stage vector quantizer

Abstract: A speech mode based multi-stage vector quantizer is disclosed which quantizes and encodes line spectral frequency (LSF) vectors that were obtained by transforming the short-term predictor filter coefficients in a speech codec that utilizes linear predictive techniques. The quantizer includes a mode classifier that classifies each speech frame of a speech signal as being associated with one of a voiced, spectrally stationary (Mode A) speech frame, a voiced, spectrally non-stationary (Mode B) speech frame and an unvoiced (Mode C) speech frame. A converter converts each speech frame of the speech signal into an LSF vector and an LSF vector quantizer includes a 12-bit, two-stage, backward predictive vector encoder that encodes the Mode A speech frames and a 22 bit, four-stage backward predictive vector encoder that encodes the Mode 13 and the Mode C speech frames.

System and method for speech recognition

Abstract: A system and method for speech recognition provides a means of printing phonemes in response to received speech signals utilizing inexpensive components. The speech signals are inputted into an amplifier which provides negative feedback to normalize the amplitude of the speech signals. The normalized speech signals are delta modulated at a first sampling rate to produce a corresponding first sequence of digital pulses. The negative feedback signal of the amplifier is delta modulated at a second sampling rate to produce a second sequence of digital pulses corresponding to amplitude information of the speech signals. The speech signals are filtered and utilized to produce a digital pulse corresponding to high frequency components of the speech signals having magnitudes in excess of a threshold voltage. A microprocessor contains an algorithm for detecting major slope transitions of the analog speech signals in response to the first sequence of digital signals by detecting information corresponding to presence and absence of predetermined numbers of successive slope reversals in the delta modulator producing the first sequence of digital pulses. The algorithm computes cues from the high frequency digital pulse and the second sequence of pulses. The algorithm computes a plurality of speech waveform characteristic ratios of time intervals between various slope transitions and compares the speech waveform characteristic ratios with a plurality of stored phoneme ratios representing a set of phonemes to detect matching therebetween. The order of comparing is determined on the basis of the cues and a configuration of a phoneme decision tree contained in the algorithm. When a matching occurs, a signal corresponding to the matched phoneme is produced and utilized to cause the phoneme to be printed. In one embodiment of the invention, the speech signals are produced by the earphone of a standard telephone headset.

Variable parameter speaker verification system based on hidden Markov modeling

Abstract: A text-independent speaker verification system based on an adaptive vocal tract model which emulates the vocal tract of the speaker is described. Each speaker is represented by a set of feature vectors derived from speech segments belonging to different classes of phonemes. Linear predictive hidden Markov modeling and maximum-likelihood Viterbi decoding are applied to a speech utterance to obtain different classes of phonemes pronounced by a speaker. It is shown that different classes of phonemes are not equally effective in discriminating between speakers and that verification performance can be considerably improved by separately classifying speech segments representing each broad phonetic category as belonging to an impostor or as belonging to the true speaker. A weighted linear combination of scores for individual categories can be used as the final verification score. The weights are chosen to reflect the effectiveness of particular classes of phonemes in discriminating between speakers and are adjusted to maximize the verification performance. >

Statistical Methods for the Enhancement of Noisy Speech

Abstract: Speech signals are frequently disturbed by statistically independent additive noise signals. When the power fluctuation of the noise signal is significantly slower than that of the speech signal, a single-microphone approach may be successfully used to reduce the level of the disturbing noise. This chapter outlines algorithms for noise reduction which are based on short term spectral representations of speech and on optimal estimation techniques. We present some of the more prominent estimation methods for complex spectral coefficients, for the amplitude and phase of spectral coefficients, and for related parameters such as the a priori signal-to-noise ratio. We interpret these algorithms in terms of their input-output characteristics. Some recent developments such as the use of super-Gaussian speech models and the properties of the resulting estimators are highlighted. Furthermore, we discuss the estimation of the background noise power and the application of these techniques in conjunction with a low bit rate speech coder.

Encoding of LPC spectral parameters using switched-adaptive interframe vector prediction (speech coding)

Abstract: An efficient, low-complexity method called switched-adaptive interframe vector prediction (SIVP) has been developed for linear predictive coding (LPC) of spectral parameters in the development of low-bit-rate speech coding systems. SIVP utilizes vector linear prediction to exploit the high frame-to-frame redundancy present in the successive frames of LPC parameters. When SIVP is combined with scalar quantization, it has been found that the LPC parameter bit rate required to achieve high-quality synthetic speech is only 1300 b/s. With vector quantization, the bit-rate can be reduced even further (to 1000 b/s) without introducing any perceivable quantization noise in the reconstructed speech. >