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Today, speech technology is only available for a small fraction of the thousands of languages spoken around the world because traditional systems need to be trained on large amounts of annotated speech audio with transcriptions. Obtaining that kind of data for every human language and dialect is almost impossible.
Wav2vec works around this limitation by requiring little to no transcribed data. The model uses self-supervision to push the boundaries by learning from unlabeled training data. This enables speech recognition systems for many more languages and dialects, such as Kyrgyz and Swahili, which don’t have a lot of transcribed speech audio. Self-supervision is the key to leveraging unannotated data and building better systems.
A. Newell, and H. Simon. Communications of the ACM, 19 (3):
113-126(March 1976)p. 116:
"The Physical Symbol System Hypothesis. A physical
symbol system has the necessary and sufficient
means for general intelligent action."
p. 120:
"Heuristic Search Hypothesis. The solutions to
problems are represented as symbol structures.
A physical symbol system exercises its intelligence
in problem solving by search--that is, by
generating and progressively modifying symbol
structures until it produces a solution structure."
p. 121:
"To state a problem is to designate (1) a test
for a class of symbol structures (solutions of the
problem), and (2) a generator of symbol structures
(potential solutions). To solve a problem is
to generate a structure, using (2), that satisfies
the test of (1).".
S. Fincher, M. Petre, and M. Clark. Springer-Verlag, London, UK, (2001)page 2: patterns as -
ä form, a "packaging", for transfer materials which does not prescribe or patronise and equally does not överwhelm with information".".
F. Kamareddine, M. Maarek, K. Retel, and J. Wells. Towards Mechanized Mathematical Assistants (Lecture Notes in AI), 4573, page 296-312. Springer, (2007)ISBN=978-3-540-73083-5,
doi = 10.1007/978-3-540-73086-6_24.