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.
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Key Features
- 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.
Website: cmusphinx.github.io
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.
Related Software
| Speech Recognition Tools | |
|---|---|
| Whisper | Automatic speech recognition (system trained on 680,000 hours of data |
| whisper.cpp | Run Whisper locally with fast, efficient C/C++ speech recognition |
| Flashlight | Fast, flexible machine learning library written entirely in C++. |
| sherpa-onnx | Offline speech recognition for many platforms and languages |
| Kaldi | C++ toolkit designed for speech recognition researchers. |
| FunASR | Versatile speech recognition toolkit for real-world audio |
| faster-whisper | Faster Whisper transcription with reduced memory use |
| SpeechBrain | All-in-one conversational AI toolkit based on PyTorch |
| Handy | Offline speech-to-text application |
| ESPnet | End-to-End speech processing toolkit |
| PocketSphinx | Lightweight speech recognition for embedded and mobile use |
| deepspeech.pytorch | Implementation of DeepSpeech2 using Baidu Warp-CTC. |
| Epicenter | Transcription application with global speech-to-text functionality |
| Julius | Two-pass large vocabulary continuous speech recognition engine |
| Speech Note | Write notes, transcribe speech, translate text and read content aloud |
| aTrain | Private local transcription of recorded speech through a polished GUI |
| hyprwhspr | Native speech-to-text designed for Arch / Omarchy |
| Vocalinux | Local voice dictation through a clean desktop interface |
| ostt | Open Speech-to-Text |
| Simon | Flexible speech recognition software |
Read our verdict in the software roundup.
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PocketSphinx looks very interesting, particualry its fixed-point arithmetic and efficient algorithms for GMM computation.