Publication

Bob Speaks Kaldi

2017
Conference Papers

Abstract

This paper introduces and demonstrates Kaldi integration into Bob signal-processing and machine learning toolbox. The motivation for this integration is two-fold. Firstly, Bob benefits from using advanced speech processing tools developed in Kaldi. Secondly, Kaldi benefits from using complementary Bob modules, such as modulation-based VAD with an adaptive thresholding. In addition, Bob is designed as an open science tool, and this integration might offer to the Kaldi speech community a framework for better reproducibility of state-of-the-art research results.

Official source

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2017
Conference Papers

Abstract

Official source

Related concepts (15)

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.

Speech synthesis

Speech synthesis is the artificial production of human speech. A computer system used for this purpose is called a speech synthesizer, and can be implemented in software or hardware products. A text-to-speech (TTS) system converts normal language text into speech; other systems render symbolic linguistic representations like phonetic transcriptions into speech. The reverse process is speech recognition. Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a database.

Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.

Related publications (4)

Bob Speaks Kaldi

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