Publication

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks

Hervé Bourlard, Mathew Magimai Doss
2000
Reports

Abstract

The challenge of automatic speech recognition (ASR) increases when speaker variability is encountered. Being able to automatically use different acoustic models according to speaker type might help to increase the robustness of ASR. We present a system that attempts to do so by augmenting the standard acoustic observations with pitch information. This allows the system to use acoustic models more appropriate to speech with the given pitch. Furthermore, pitch information is more easily detected in noisy conditions; thus, it may be of use in robust speech recognition. Using dynamic Bayesian networks (DBNs) allows further refinement of the system by eliminating unnecessary statistical dependencies and thus reducing the number of parameters. We show that when a system is trained on observed pitch data and performs recognition with missing pitch data, it can perform significantly better than a system that uses acoustics information only.

Official source

https://infoscience.epfl.ch/entities/publication/ae10b88e-1366-4037-8e9c-4becc0d282b0

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, Mathew Magimai Doss
2000
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ae10b88e-1366-4037-8e9c-4becc0d282b0

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.

Pitch class

In music, a pitch class (p.c. or pc) is a set of all pitches that are a whole number of octaves apart; for example, the pitch class C consists of the Cs in all octaves. "The pitch class C stands for all possible Cs, in whatever octave position." Important to musical set theory, a pitch class is "all pitches related to each other by octave, enharmonic equivalence, or both." Thus, using scientific pitch notation, the pitch class "C" is the set {Cn : n is an integer} = {..., C−2, C−1, C0, C1, C2, C3 ...}.

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.

Related publications (32)

Automatic Speech Recognition using Pitch Information in Dynamic Bayesian Networks

Chat with Graph Search

Comparing Self-Supervised Learning Models Pre-Trained on Human Speech and Animal Vocalizations for Bioacoustics Processing

Desi Dr2 Results. Ii. Measurements of Baryon Acoustic Oscillations and Cosmological Constraints

Segment-level training of ANNs based on acoustic confidence measures for hybrid HMM/ANN Speech Recognition