Topic
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.
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Patent•
27 Sep 1995
TL;DR: In this paper, a broad-band speech signal from a narrow-band voice signal was obtained by efficiently using an existing telephone system. But this method consists of such processes as a 16kHz sampling processing 101 for 8kHz sampling narrow-bands speech signal, a full-wave rectification processing 102 for up-sampled speech signal and a short-time Fourier analysis processing 103 for the full-wound rectification processed speech signal.
Abstract: PROBLEM TO BE SOLVED: To obtain a broad-band speech signal from a narrow-band voice signal by efficiently using an existing telephone system. SOLUTION: This method consists of such processes as a 16kHz sampling processing 101 for 8kHz sampling narrow-band speech signal, a full-wave rectification processing 102 for up-sampled speech signal, a short-time Fourier analysis processing 103 for the full-wave rectification processed speech signal, a band-pass filtering processing 104 for low range of the complex spectra, a processing 106 to obtain a high range complex spectra from the complex spectra obtained by the processing 105, a STFT synthesis processing 109 to synthesize the complex spectra of the high and low ranges, and an addition processing 110 to sum the up-sampled speech signal and the STFT synthesized speech signal.
31 citations
Patent•
21 Jul 2013
TL;DR: In this article, a speech signal can be characterized and the characterization can be employed to improve ASR performance in a noisy environment, such as a media device such as smart TV.
Abstract: Systems and methods are provided for enhancing speech signal intelligibility and for bettering performance of automatic speech recognition processes, for a speech signal in a noisy environment. Some typical application environments include a media device such as a smart TV. An acoustically coupled loudspeaker signal and signals from one or more microphones can be employed to enhance a near end user speech signal. Some processing can be application-specific, such as specific to applications wherein cleaned speech is employed for human voice communication and/or specific to applications employing Automatic Speech Recognition (ASR) processing. A formant emphasis filter and a spectrum band reconstruction process can be employed to enhance speech quality and/or to improve ASR recognition rate performance. A speech signal can be characterized and the characterization can be employed to improve ASR performance. Some systems and methods apply to devices having a foreground microphone and a background microphone.
31 citations
15 Apr 2007
TL;DR: The results reveal that the proposed method is more reliable and less sensitive to mode of signal acquisition and unforeseen conditions.
Abstract: The goal of this work is to provide robust and accurate speech detection for automatic speech recognition (ASR) in meeting room settings. The solution is based on computing long-term modulation spectrum, and examining specific frequency range for dominant speech components to classify speech and non-speech signals for a given audio signal. Manually segmented speech segments, short-term energy, short-term energy and zero-crossing based segmentation techniques, and a recently proposed multi layer perceptron (MLP) classifier system are tested for comparison purposes. Speech recognition evaluations of the segmentation methods are performed on a standard database and tested in conditions where the signal-to-noise ratio (SNR) varies considerably, as in the cases of close-talking headset, lapel, distant microphone array output, and distant microphone. The results reveal that the proposed method is more reliable and less sensitive to mode of signal acquisition and unforeseen conditions.
31 citations
05 Jun 2000
TL;DR: From speaker-independent connected digit HMM recognition, it is found that the speech recognition system employing the proposed bitstream-based front-end gives superior word and string accuracies over a recognizer constructed from decoded speech signals.
Abstract: In this paper, we propose a feature extraction method for a speech recognizer that operates in digital communication networks. The feature parameters are basically extracted by converting the quantized spectral information of a speech coder into a cepstrum. We also combine the voiced/unvoiced information obtained from the bitstream of the speech coder into the recognition feature set. From speaker-independent connected digit HMM recognition, we find that the speech recognition system employing the proposed bitstream-based front-end gives superior word and string accuracies over a recognizer constructed from decoded speech signals. Its performance is comparable to that of the wireline recognition system that uses only the cepstrum as a feature set.
31 citations
01 Jan 2001
TL;DR: Both types of score normalization significantly improve performance, and can eliminate the performance loss that occurs when there is a mismatch between training and testing conditions.
Abstract: We investigate the effect of speech coding on automatic speaker recognition when training and testing conditions are matched and mismatched. Experiments used standard speech coding algorithms (GSM, G.729, G.723, MELP) and a speaker recognition system based on Gaussian mixture models adapted from a universal background model. There is little loss in recognition performance for toll quality speech coders and slightly more loss when lower quality speech coders are used. Speaker recognition from coded speech using handset-dependent score normalization and test score normalization are examined. Both types of score normalization significantly improve performance, and can eliminate the performance loss that occurs when there is a mismatch between training and testing conditions.
31 citations