OpenEars - iPhone Voice Recognition and Text-To-Speech. Dynamically generate a JSGF grammar using OpenEars' natural language system for defining a speech recognition ruleset. The NSDictionary you submit to the argument generateGrammarFromDictionary: is a key-value pair consisting of an NSArray of words stored in NSStrings indicating the vocabulary to be listened for, and an NSString key which is one of the following #defines from GrammarDefinitions.h, indicating the rule for the vocabulary in the NSArray: ThisWillBeSaidOnce ThisCanBeSaidOnce ThisWillBeSaidWithOptionalRepetitions ThisCanBeSaidWithOptionalRepetitions OneOfTheseWillBeSaidOnce OneOfTheseCanBeSaidOnce OneOfTheseWillBeSaidWithOptionalRepetitions OneOfTheseCanBeSaidWithOptionalRepetitions Breaking them down one at a time for their specific meaning in defining a rule: ThisWillBeSaidOnce // This indicates that the word or words in the array must be said (in sequence, in the case of multiple words), one time.
ThisWillBeSaidOnce : @[ CMUSphinx Wiki - CMUSphinx Wiki. This page contains collaboratively developed documentation for the CMU Sphinx speech recognition engines. This section contains links to documents which describe how to use Sphinx to recognize speech. Currently, we have very little in the way of end-user tools, so it may be a bit sparse for the forseeable future.
See also some more docs: If you want to find out where CMUSphinx works, see These documents either describe some particular aspect of the Sphinx codebase in detail, or they serve as a developer's guide to accomplishing some particular task. Sphinx4 Space : Information about sphinx4, design, code, performance, history. These documents describe the excruciating detail of APIs, or provide other useful background information for CMUSphinx developers. This section contains various internal information for CMUSphinx developers. File formats Materials for GSOC This section tries to collect research ideas for specific problems in speech recognition. Fingerprinting. Musicbrainz has used several audio fingerprinting systems over its lifetime. All of them (so far) work in essentially the same way. It is generally a two-step process of submission and lookup. First, the raw audio is used to create a fingerprint, which is then submitted to a third-party server.
This server analyzes the fingerprint, compares it to other fingerprints, and decides whether it is sufficiently different from known fingerprints as to issue a new ID. Once this step is done, a fingerprint can be calculated for any file and this can be used to look up the corresponding ID. This ID is associated with a given track (pre-NGS) or recording (post-NGS), and metadata can be gathered from there. TRM (TRM Recognizes Music) IDs were MusicBrainz’ first audio fingerprinting system. This system was used in the original musicbrainz tagger application. TRM support was removed in November 2008[3]. PUIDs are Musicbrainz’ second audio fingerprinting system. AcoustID It has several immediate advantages:
Open source music identification. How it works Echoprint “listens” to audio on a phone or on your computer to figure out what song it is. It does so very fast and with such good accuracy that it can identify very noisy versions of the original or recordings made on a mobile device with a lot of interference from outside sources. Since anyone can use Echoprint for free or install their own servers, we expect that it will become the de facto music identification technology. And since all the data is available, we expect Echoprint to quickly be able to resolve every song in the world. Technical details Echoprint consists of three parts: the code generator, which converts audio into codes, the server, which stores and indexes codes, and the data, which comes from partners and other Echoprint users. The code generator computes {time, hash} pairs from an audio signal using advanced signal processing to account for noise and modification.
Accuracy details. Open source music identification.