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Awesome GitHub RepositoriesMixture Selection Models
Extracts probability density functions from HMMs to accelerate the selection process during real-time decoding.
Distinct from Speech Recognition Engines: Distinct from Speech Recognition Engines: focuses on the specific mixture selection optimization within the HMM decoding process.
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Awesome Mixture Selection Models GitHub Repositories
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- julius-speech/julius
julius-speech/julius
1,927在 GitHub 上查看Julius is a high-performance, open-source speech recognition engine designed for large vocabulary continuous speech recognition. It functions as a comprehensive framework utilizing Hidden Markov Model-based acoustic modeling and N-gram language models to convert live or recorded audio into text. The engine is built to support real-time streaming and provides a network-accessible service that allows external applications to manage recognition sessions and receive transcription results through programmatic commands. The engine distinguishes itself through its modular architecture and support fo
Extracts probability density functions from HMM definitions to speed up the selection process during real-time recognition.
Caudio-processingrecognitionspeech
