Open Source Speech Recognition Software - Page 4

This project provides an implementation of duration high-order hidden Markov model (DHO-HMM) in Java. It is compactible with JDK 5 & 6. It was used in the author's research on speech recognition of Mandarin digits. There are some Chinese words in this project and I am afraid that I don't have enough time to translate to English recently.

Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition.

Augmenting other natural interfaces namely handwriting, speech recognition to input Indian language characters to the computer. Speech synthesis is also provided to read out local language text.

InproTK is an Incremental Spoken Dialogue Processing Toolkit, that is, a toolkit to help you build dialogue systems that listen and talk incrementally, allowing for advanced interactional behaviour. Please see our Wiki for more information: http://sourceforge.net/p/inprotk/wiki/

Project aim to provide simple easy APIs for Java developers to use interactive abilities in their Java Applications like speech recognition, handwriting recognition, use of web cam , sound record/play, decision trees , text to speech and many others.

The JSGF Editor supports development of grammars for speech recognition with the Java Speech Grammar Format. It is provided as a plugin for Eclipse 3.* (tested on 3.1 and 3.2)

This software is used to add interoperability between the Jack audio connection kit and Julius speech recognition software.

Working template to create an Asterisk IVR system using kaldi for speech recognition. IVR based speech recognition.

This project provides a gesture based movement of part objects in the CAD-system CATIA. It is possible to rotate, move or zoom in or out. Further there is a rudimentary speech recognition to change the rotating axes or to do some other helpful things. KinectCAD has been written in Visual C# 2010. The package includes the source code and binaries files. To start KinectCAD it is necessary to have a correct installed Microsoft Kinect. Also it is helpful if there is an installed Kinect SDK V1. But you can also download the Runtime at: http://download.microsoft.com/download/E/E/2/EE2D29A1-2D5C-463C-B7F1-40E4170F5E2C/KinectRuntime-v1.0-Setup.exe Important! The program is still in development! In future there will be several updates. Until now there is a handbook and several other documents to understand KinectCAD which are not translated to english but in the near future I will translate them. http://www.youtube.com/watch?v=A3ZYCyCt75g If there are questions or problems mail me.

Language Tutor helps any language student in English, Spanish & French, to master that language. Language tutor has Speech Recognition ability, A student can Listen, Read, Write, Comprehend and Self Evaluate.

The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information. The essential problem is to find the correspondence between the audio and visual streams, which is the goal of this work. We proposed the utilization of a coupled 3D Convolutional Neural Network (CNN) architecture that can map both modalities into a representation space to evaluate the correspondence of audio-visual streams using the learned multimodal features.

A learning package for children, helping them to learn a foreign language. Techniques such as speech recognition will be used.

This project will show how to implement the Hidden Markov Model approximations of Voice Recognition into embedded and low power systems.

Automated speech recognition software is extremely cumbersome. This project's aim is to incrementally improve the quality of an open-source and ready to deploy speech to text recognition system. Runs on Windows using the mdictate.exe, but the core workings are found in the mdictate.py script which should work on Windows/Linux/OS X. In version 1.0, we use pocketsphinx' default setup with a basic graphic interface.

The MRCPv2 protocol is designed to allow client devices to control media processing resources, such as speech recognition engines. MRCP4J provides a Java API that encapsulates the MRCPv2 protocol and can be used to implement MRCP clients and/or servers.

MAIA (MyApp Intelligence Artificial) is designed to provide a foundation for building your own voice-controlled assistant with Python. It uses various libraries and modules for speech recognition, text-to-speech synthesis, and custom functionality.

Programm to use system by voice recognition; Maked by Arhipow Stanislaw; Language C#

Multiplayer Shared Problem solving Neural Network AI Simulation, Chat / Speech Recognition / Customizable Identities / GUI Chat in real time with users and their developed AI Real time Simulation. NNET FANN Blockchain integration

The aim of this project is to develop a software to assist speech recognition systems users by displaying outputs of their systems.

OC Volume is a speech recognition engine written in Java for integration with other applications. It is currently an User-Dependent Isolated Word Recognizer and can be expanded to include more capability for recognition.

Ma sta bre public, nikomu nedamo!

Open Source Speech Recognition

Automatic Speech Recognition using: -HTK toolkit -TIDIGIT (Connected Digits Texas Instruments) -HMM method -VTLN method

Omnilingual-ASR is a research codebase exploring automatic speech recognition that generalizes across a very large number of languages using shared modeling and training recipes. It focuses on leveraging self-supervised audio pretraining and scalable fine-tuning so low-resource languages can benefit from high-resource data. The project provides data preparation pipelines, training scripts, decoding utilities, and evaluation tools so researchers can reproduce results and extend to new language sets. It emphasizes modularity: acoustic modeling, language modeling, tokenization, and decoding are separable pieces you can swap or ablate. The repo is aimed at pushing practical multilingual ASR—robust to accents, code-switching, and domain shifts—rather than language-by-language systems. For practitioners, it’s a starting point to study transfer, zero-shot behavior, and trade-offs between model size, compute cost, and coverage.