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Vocal Features: From Voice Identification to Speech Recognition by Machine

Authors: Li, X.; Mills, M.;

Vocal Features: From Voice Identification to Speech Recognition by Machine

Abstract

This article considers machine methods used in the collection, processing, and application of vocal recordings for speaker identification and speech recognition between 1908 and 1970. The first phonographic archives featured collections of "vocal portraits" that prompted international investigations into the essential features of human voices for individual identification. Visual records of speech later found the same applications, but as "voiceprint identification" via sound spectrography began to achieve legal and commercial success in the 1960s, the procedure attracted more widespread scientific attention, which ultimately discredited both its accuracy and its rationale. At the same time, spectrogram collections spurred a new application-speech recognition by machine. The changing status of the speech spectrogram, from a record of unique features of individual voices to a model of fundamental invariants in speech sounds, was rooted in the demands of automated processing and a corresponding shift from the sound archive to the acoustic database.

Related Organizations
Keywords

Sound Spectrography, Phonetics, Forensic Sciences, Speech Perception, Voice, Humans, Speech, History, 20th Century, Speech Acoustics

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
41
Top 10%
Top 10%
Top 10%
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