Publication

Posteriori Probabilities and Likelihoods Combination for Speech and Speaker Recognition

Hervé Bourlard
2004
Reports

Abstract

This paper investigates a new approach to perform simultaneous speech and speaker recognition. The likelihood estimated by a speaker identification system is combined with the posterior probability estimated by the speech recognizer. So, the joint posterior probability of the pronounced word and the speaker identity is maximized. A comparison study with other standard techniques is carried out in three different applications, (1) closed set speech and speaker identification, (2) open set speech and speaker identification and (3) speaker quantization in speaker-independent speech recognition.

Official source

https://infoscience.epfl.ch/entities/publication/de23f547-f64f-4baa-9940-597e0baf8a06

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Hervé Bourlard
2004
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/de23f547-f64f-4baa-9940-597e0baf8a06

About this result

Related concepts (32)

Speaker recognition

Speaker recognition is the identification of a person from characteristics of voices. It is used to answer the question "Who is speaking?" The term voice recognition can refer to speaker recognition or speech recognition. Speaker verification (also called speaker authentication) contrasts with identification, and speaker recognition differs from speaker diarisation (recognizing when the same speaker is speaking).

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.

Posterior probability

The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a scientific hypothesis, or parameter values), given prior knowledge and a mathematical model describing the observations available at a particular time.

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Posteriori Probabilities and Likelihoods Combination for Speech and Speaker Recognition

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