Publication

Automatic Speech Recognition using Dynamic Bayesian Networks with both Acoustic and Articulatory Variables

Hervé Bourlard
2000
Conference Papers

Abstract

Current technology for automatic speech recognition (ASR) uses hidden Markov models (HMMs) that recognize spoken speech using the acoustic signal. However, no use is made of the causes of the acoustic signal: the articulators. We present here a dynamic Bayesian network (DBN) model that utilizes an additional variable for representing the state of the articulators. A particular strength of the system is that, while it uses measured articulatory data during its training, it does not need to know these values during recognition. As Bayesian networks are not used often in the speech community, we give an introduction to them. After describing how they can be used in ASR, we present a system to do isolated word recognition using articulatory information. Recognition results are given, showing that a system with both acoustics and inferred articulatory positions performs better than a system with only acoustics.

Official source

https://infoscience.epfl.ch/entities/publication/28780bb5-21a6-4769-83da-c37259288b69

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Hervé Bourlard
2000
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/28780bb5-21a6-4769-83da-c37259288b69

About this result

Related concepts (32)

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.

Bayesian network

A Bayesian network (also known as a Bayes network, Bayes net, belief network, or decision network) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). It is one of several forms of causal notation. Bayesian networks are ideal for taking an event that occurred and predicting the likelihood that any one of several possible known causes was the contributing factor. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms.

Dynamic Bayesian network

A Dynamic Bayesian Network (DBN) is a Bayesian network (BN) which relates variables to each other over adjacent time steps. A Dynamic Bayesian Network (DBN) is often called a Two-Timeslice BN (2TBN) because it says that at any point in time T, the value of a variable can be calculated from the internal regressors and the immediate prior value (time T-1). DBNs were developed by Paul Dagum in the early 1990s at Stanford University's Section on Medical Informatics.

Related publications (32)

Automatic Speech Recognition using Dynamic Bayesian Networks with both Acoustic and Articulatory Variables

Chat with Graph Search

Neural Galerkin Normalizing Flows for Bayesian Inference of Diffusions with Inaccessible Boundaries

On the Sample Complexity of Next-token Prediction

Domain Adaptation and Investigation of Robustness of DNN-based Embeddings for Text-Independent Speaker Verification Using Dilated Residual Networks