Publication

The Juicer LVCSR Decoder - User Manual for Juicer version 0.5.0

2006
Reports

Abstract

Juicer is a decoder for HMM-based large vocabulary speech recognition that uses a weighted finite state transducer (WFST) representation of the search space. The package consists of a number of command line utilities: the Juicer decoder itself, along with a number of tools and scripts that are used to combine the various ASR knowledge sources (language model, pronunciation dictionary, acoustic models) into a single, optimised WFST that is input to the decoder.

Official source

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2006
Reports

Abstract

Official source

Related concepts (28)

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Language model

A language model is a probabilistic model of a natural language that can generate probabilities of a series of words, based on text corpora in one or multiple languages it was trained on. Large language models, as their most advanced form, are a combination of feedforward neural networks and transformers. They have superseded recurrent neural network-based models, which had previously superseded the pure statistical models, such as word n-gram language model.

Large language model

A large language model (LLM) is a language model characterized by its large size. Their size is enabled by AI accelerators, which are able to process vast amounts of text data, mostly scraped from the Internet. The artificial neural networks which are built can contain from tens of millions and up to billions of weights and are (pre-)trained using self-supervised learning and semi-supervised learning. Transformer architecture contributed to faster training.

Related publications (7)

The Juicer LVCSR Decoder - User Manual for Juicer version 0.5.0

Chat with Graph Search

Advancing Neural Representations for Paralinguistic Analysis: From Speech Emotion to Parkinson's Disease Assessment

The Physics of Data and Tasks: Theories of Locality and Compositionality in Deep Learning

Exploiting semi-supervised training through a dropout regularization in end-to-end speech recognition