ITU-T G.729語音壓縮算法。
description:
Fixed-point description of commendation G.729 with ANNEX B Coding of speech at 8 kbit/s using Conjugate-Structure Algebraic-Code-Excited Linear-Prediction (CS-ACELP) with Voice Activity Decision(VAD), Discontinuous Transmission(DTX), and Comfort Noise Generation(CNG).
Hidden_Markov_model_for_automatic_speech_recognition
This code implements in C++ a basic left-right hidden Markov model
and corresponding Baum-Welch (ML) training algorithm. It is meant as
an example of the HMM algorithms described by L.Rabiner (1) and
others. Serious students are directed to the sources listed below for
a theoretical description of the algorithm. KF Lee (2) offers an
especially good tutorial of how to build a speech recognition system
using hidden Markov models.
AutoSummary uses Natural Language Processing to generate a contextually-relevant synopsis of plain text.
It uses statistical and rule-based methods for part-of-speech tagging, word sense disambiguation,
sentence deconstruction and semantic analysis.
Digital Signal and Image Processing Using MATLAB
The most important theoretical aspects of image and signal processing (ISP) for both deterministic and random signals are covered in this guide to using MATLAB® . The discussion is also supported by exercises and computer simulations relating to real applications such as speech processing and fetal-heart–rhythm tracking, and more than 200 programs and functions for numerical experiments are provided with commentary.
Matsig is an object-oriented signal class library for MATLAB 6.5 and later. It implements a signal class, simplifying operations and manipulations common in audio signal processing and speech processing
With the advent of multimedia, digital signal processing (DSP) of sound has emerged from the shadow of bandwidth-limited speech processing. Today, the main appli- cations of audio DSP are high quality audio coding and the digital generation and manipulation of music signals. They share common research topics including percep- tual measurement techniques and analysis/synthesis methods. Smaller but nonetheless very important topics are hearing aids using signal processing technology and hardware architectures for digital signal processing of audio. In all these areas the last decade has seen a significant amount of application oriented research.
Many problems in statistical pattern recognition begin with the preprocessing of multidimensional signals, such as images of faces or spectrograms of speech.
