CMUSphinx – Open Source Speech Recognition System for Mobile and Server Applications

CMUSphinx (Sphinx) is a collective term to describe a group of speech recognition systems developed at Carnegie Mellon University.

CMUSphinx contains a number of packages for different tasks and applications:

  • Pocketsphinx — a lightweight speech recognition engine, specifically tuned for handheld and mobile devices, written in C.
  • Sphinxbase — contains the basic libraries shared by the CMU Sphinx trainer and all the Sphinx decoders (Sphinx-II, Sphinx-III, and PocketSphinx), as well as some common utilities for manipulating acoustic feature and audio files.
  • Sphinx4 — a state-of-the-art, speaker-independent, continuous speech recognition system written in the Java programming language. The design of Sphinx-4 is based on patterns that have emerged from the design of past systems as well as new requirements based on areas that researchers currently want to explore. To exercise this framework, and to provide researchers with a “research-ready” system, Sphinx-4 also includes several implementations of both simple and state-of-the-art techniques.
  • Sphinxtrain — Carnegie Mellon University’s open source acoustic model trainer.

Features of the toolkit include:

  • State of art speech recognition algorithms for efficient speech recognition. CMUSphinx tools are designed specifically for low-resource platforms.
  • A flexible design.
  • Focuses on practical application development and not on research.
  • Wide range of tools for many speech-recognition related purposes (keyword spotting, alignment, pronunciation evaluation).
  • Support for several languages including English, French, Mandarin, German, Dutch, Russian, and the ability to build models for other languages.

Support: FAQ, GitHub
Developer: Many contributors
License: BSD-like license

Sphinx is written in Java. Learn Java with our recommended free books and free tutorials.

Return to Speech Recognition Tools Home Page

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One comment

  1. PocketSphinx looks very interesting, particualry its fixed-point arithmetic and efficient algorithms for GMM computation.

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