Julius: an open source realtime large vocabulary recognition engine. ”Julius” is a high-performance, small-footprint large vocabulary continuous speech recognition (LVCSR) decoder software for speech-related researchers and developers. Based on word N-gram and context-dependent HMM, it can perform real-time decoding on various computers and devices from micro-computer to cloud server. The algorithm is based on 2-pass tree-trellis search, which fully incorporates major decoding techniques such as tree-organized lexicon, 1-best / word-pair context approximation, rank/score pruning, N-gram factoring, cross-word context dependency handling, enveloped beam search, Gaussian pruning, Gaussian selection, etc. Besides search efficiency, it is also modularized to be independent from model structures, and wide variety of HMM structures are supported such as shared-state triphones and tied-mixture models, with any number of mixtures, states, or phone sets. It also can run multi-instance recognition, running dictation, grammar-based recognition or isolated word recognition simultaneously in a single thread. Standard formats are adopted for the models to cope with other speech / language modeling toolkit such as HTK, SRILM, etc. Recent version also supports Deep Neural Network (DNN) based real-time decoding.

References in zbMATH (referenced in 5 articles )

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  1. Mirco Ravanelli; Titouan Parcollet; et al: SpeechBrain: A General-Purpose Speech Toolkit (2021) arXiv
  2. Sas, Jerzy; Żołnierek, Andrzej: Pipelined language model construction for Polish speech recognition (2013) ioport
  3. Oka, Tetsushi; Sugita, Kaoru; Yokota, Masao: Commanding a humanoid to move objects in a multimodal language (2010) ioport
  4. Oka, Tetsushi; Abe, Toyokazu; Sugita, Kaoru; Yokota, Masao: RUNA: a multimodal command language for home robot users (2009) ioport
  5. Oka, Tetsushi; Abe, Toyokazu; Sugita, Kaoru; Yokota, Masao: Success rates in a multimodal command language for home robot users (2009) ioport