Google summer of code is definitely one of the largest projects in open source world. 1400 students will enjoy the participation in the open source projects during this summer. Four projects of the pool are dedicated to speech recognition and it is really amazing all of them are planning to use CMUSphinx!

Here is the list of new hot projects for you to track and participate:

Speech to Text Enhancement Engine for Apache Stanbol
Student: Suman Saurabh
Organization: Apache Software Foundation
Assigned mentors: Andreas Kuckartz

Enhancement engine uses Sphinix library to convert the captured audio. Media (audio/video) data file is parsed with the ContentItem and formatted to proper audio format by Xuggler libraries. Audio speech is than extracted by Sphinix to 'plain/text' with the annotation of temporal position of the extracted text. Sphinix uses acoustic model and language model to map the utterances with the text, so the engine will also provide support of uploading acoustic model and language model.

Development of both online as offline speech recognition to B2G and Firefox

Student:Andre Natal
Organization: Mozilla
Assigned mentors: Guilherme Gonçalves
Short description: Mozilla needs to fill the gap between B2G and other mobile OSes, and also desktop Firefox lacks this important feature already available at Google Chrome. In addition, we’ll have a new Web API empowering developers , and every speech recognition application already developed and running on Chrome, will start to work on Firefox without changes. On future, this can be integrated on other Mozilla products, opening windows to a whole new class of interactive applications.

I know Andre very well, he is a very talented person, so I'm sure this project will be a huge success. Between, you can track it in github repository too: https://github.com/andrenatal/speechrtc

Sugar Listens - Speech Recognition within the Sugar Learning Platform

Student: Rodrigo Parra
Organization: Sugar Labs
Assigned mentors: tchx84
Short description: Sugar Listens seeks to provide an easy-to-use speech recognition API to educational content developers, within the Sugar Learning Platform. This will allow developers to integrate speech-enabled interfaces to their Sugar Activities, letting users interact with Sugar through voice commands. This goal will be achieved by exposing the open-source speech recognition engine Pocketsphinx as a D-Bus service.

Integrate Plasma Media Center with Simon to make navigation easier

Student: Ashish Madeti
Organization: KDE
Assigned mentors: Peter Grasch, Shantanu Tushar
Short description: User can currently navigate with keyboard and mouse in Plasma Media Center. Now, I will add Voice as a way for a user to navigate and use PMC. This will be done by integrating PMC with Simon.

I know Simon has a large and successful history of GSOC participation, so this project is also going to be very interesting.

Also, this summer we are going to run few student internships unrelated to GSOC, it's going to be very interesting too, stay tuned!

Personal assistants are hot these days. Open source personal assistant is a dream for many developers. Recently released Jasper makes it really easy to install personal assistant on Raspberry Pi and use it for custom voice commands, information retrieval and so on. Jasper is written in Python and can be extended through the API. More importantly, Jasper uses CMUSphinx for offline speech recognition, so much waited capability for assistant developers.

Try Jasper, download it's source code from Github, modify it according to your needs and contribute new features.

We wish all the best to Jasper project and hope it will become popular.

It's great that more and more applications are using CMUSphinx. Recently a smart picture voice-annotation/tagging Android app using speech recognition to automatically generate multiple suggestion has been announced. Smile uses CMU Sphinx4 as one of the ASR recognizers.

Smile is a camera and picture gallery application that helps you to immediately voice-annotate your photos. Smile will then automatically extract the text (annotation) from the recorded voice using speech recognition technology, and will embed the text annotation and the voice as metadata inside the picture. So if you are searching for a picture in the hundreds of pictures you have on your phone, you can find them easily using Smile's Search facility. Even more, with Smile you can share the photo with friends, with or without the speech and text. Your friends or colleagues can see the picture (if they don't have Smile), and with Smile they can see the picture, the text and listen to the speech. Great for fun, quick greeting photos, work if you need to take photos with record to cherish memories, for documentation purposes, expense receipts, and many more uses.

To play with Smile you can download it at Google Play Store.

It is not easy to build an intelligent software, the cooperation on all the levels of the speech processing needs to be tight. For example, you should not only recognize the input but also understand it and, more importantly, respond to it. You need to give your software an ability to understand the current situation, correct the results and respond intelligently. A great piece of software to assist you with that has been recently release.

OpenDial is a Java-based software toolkit that can be used to develop robust and adaptive dialogue systems for various domains. Dialogue understanding, management and generation models are expressed in OpenDial through probabilistic rules encoded in a simple XML format.

You can find more information about the toolkit on the project website:

http://opendial.googlecode.com

The current release contains a completely refactored code base, a large set of unit tests and a full-fledged graphical interface. You can also find on the website practical examples of dialogue domains and a step-by-step documentation on how to use the toolkit.

Try it!