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` UNITED STATES PATENT AND TRADEMARK OFFICE
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`BEFORE THE PATENT TRIAL AND APPEAL BOARD
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`APPLE INC.,
`Petitioner,
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`v.
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`Zentian Limited
`Patent Owner.
`____________________
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`Case IPR2023-00037
`Patent No. 10,971,140
`____________________
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`DECLARATION OF DELIANG WANG, Ph.D., IN SUPPORT OF
`PATENT OWNER’S PRELIMINARY RESPONSE
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`Case IPR2023-00037
`DECLARATION OF DELIANG WANG, PH.D
`TABLE OF CONTENTS
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`Introduction
`Engagement
`Background and qualifications
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`I.
`1
`A.
`1
`B.
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`C. Materials considered
`3
`II. Relevant legal standards
`4
`III. Overview of the ’140 Patent
`8
`IV. Replacing Jiang’s generic processor with Chen’s supercomputer processing
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`V. Using each of Chen’s shared cluster memories as an acoustic model memory
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`VI. It would not have been obvious to configure each of Chen’s eight or more
`19
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`Person of ordinary skill in the art
`A.
`Burden of proof
`B.
`Claim construction
`C.
`D. Obviousness
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`4
`6
`6
`7
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`architecture would not have been obvious
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`for storing acoustic model data would not have been obvious
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`processors “to compute a probability” as recited in the challenged claims
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`- i -
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`I, DeLiang Wang, Ph.D, do hereby declare as follows:
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`I.
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`Introduction
`A.
`Engagement
`1.
`I have been retained by Patent Owner Zentian Limited (“Zentian” or
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`“Patent Owner”) to provide my opinions with respect to Zentian’s Preliminary
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`Response to the Petition in Inter Partes Review proceeding IPR2023-00037, with
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`respect to U.S. Pat. 10,971,140. I am being compensated for my time spent on this
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`matter. I have no interest in the outcome of this proceeding and the payment of my
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`fees is in no way contingent on my providing any particular opinions.
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`2.
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`As part of this engagement, I have also been asked to provide my
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`technical review, analysis, insights, and opinions regarding the materials cited and
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`relied upon by the Petition, including the prior art references and the supporting
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`Declaration of Mr. Schmandt.
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`3.
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`The statements made herein are based on my own knowledge and
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`opinions.
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`Background and qualifications
`B.
`4. My full qualifications, including my professional experience and
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`education, can be found in my Curriculum Vitae, which includes a complete list of
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`my publications, and is attached as Ex. A to this declaration.
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`5.
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`I have spent my professional and academic career as a researcher in
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`the field of speech processing and machine learning (including deep learning). I
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`am currently a University Distinguished Scholar and Professor in the Department
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`of Computer Science and Engineering at Ohio State University.
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`6.
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`I received B.S. in 1983 and M.S. in 1986 from Peking (Beijing)
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`University, both in computer science. I received a Ph.D. in computer science in
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`1991 from the University of Southern California.
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`7.
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`I have received numerous awards and honors, including the U.S.
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`Office of Naval Research Young Investigator Award, the Best Paper Awards from
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`the Institute of Electrical and Electronics Engineers (“IEEE”) Computational
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`Intelligence Society and the IEEE Signal Processing Society, and the Helmholtz
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`Award from the International Neural Network Society. I am Co-Editor-in-Chief of
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`Neural Networks, a premier journal in the field of neural networks and deep
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`learning, and also served as President of the International Neural Network Society.
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`8.
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`I am an IEEE Fellow and an ISCA Fellow. I have published 175
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`articles in major scientific journals and more than 250 papers in leading conference
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`proceedings. In addition, I have supervised 29 graduate students who earned their
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`PhDs in computer science and engineering, including those currently employed by
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`leading IT companies preforming ASR and related work. More details are given in
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`the attached curriculum vitae.
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`9.
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`I am a recognized expert in the field of robust ASR (automatic speech
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`recognition) technology, including scientific methods, and algorithm development
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`and testing. Robust ASR aims to develop ASR algorithms that can suppress, or
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`remain unaffected by, background interference (such as noise). ASR algorithms
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`developed in my laboratory have been recognized as some of the best in the world;
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`our algorithms achieved the highest recognition rate in the CHiME-2 challenge in
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`2016, in the CHiME-4 challenge in 2020, and the LibriCSS challenge in 2021. My
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`research contributions and achievements in the fields of speech processing were
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`featured in the March 2017 issue of IEEE Spectrum, the most circulated technical
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`magazine in the world. I am one of the most published authors in peer-reviewed
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`scientific journals in the fields of speech and audio processing.
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`C. Materials considered
`In the course of preparing my opinions, I have reviewed and am familiar
`10.
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`with the ’140 patent, including its written description, figures, and claims. I have
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`also reviewed and am familiar with the Petition in this proceeding, the supporting
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`Declaration of Mr. Schmandt, and the relied upon prior art, including Jiang and
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`Chen. I have also reviewed the materials cited in this declaration. My opinions are
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`based on my review of these materials as well as my 30 years of experience, research,
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`and education in the field of art.
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`II. Relevant legal standards
`I am not an attorney. I offer no opinions on the law. But counsel has
`11.
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`informed me of the following legal standards relevant to my analysis here. I have
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`applied these standards in arriving at my conclusions.
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`A.
`12.
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`Person of ordinary skill in the art
`I understand that an analysis of the claims of a patent in view of prior
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`art has to be provided from the perspective of a person having ordinary skill in the
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`art at the time of invention of the ’140 patent. I understand that I should consider
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`factors such as the educational level and years of experience of those working in the
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`pertinent art; the types of problems encountered in the art; the teachings of the prior
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`art; patents and publications of other persons or companies; and the sophistication
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`of the technology. I understand that the person of ordinary skill in the art is not a
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`specific real individual, but rather a hypothetical individual having the qualities
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`reflected by the factors discussed above.
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`13.
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`I understand that the Petition applies a priority date of February 4, 2002,
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`for the challenged claims, Pet. 5, and I apply the same date.
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`14.
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`I further understand that the Petition defines the person of ordinary skill
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`in the art at the time of the invention as having had a master’s degree in computer
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`engineering, computer science, electrical engineering, or a related field, with at least
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`two years of experience in the field of speech recognition, or a bachelor’s degree in
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`the same fields with at least four years of experience in the field of speech
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`recognition. The Petition adds that further education or experience might substitute
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`for the above requirements. I do not dispute the Petition’s assumptions at this time,
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`and my opinions are rendered on the basis of the same definition of the ordinary
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`artisan set forth in the Petition.
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`15.
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`I also note, however, that an ordinarily skilled engineer at the time of
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`the invention would have been trained in evaluating both the costs and benefits of a
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`particular design choice. Engineers are trained (both in school and through general
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`experience in the workforce) to recognize that design choices can have complex
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`consequences that need to be evaluated before forming a motivation to pursue a
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`particular design choice, and before forming an expectation of success as to that
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`design choice. In my opinion, anyone who did not recognize these realities would
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`not be a person of ordinary skill in the art. Thus, a person who would have simply
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`formed design motivations based only on the premise that a particular combination
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`of known elements would be possible would not be a person of ordinary skill
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`regardless of their education, experience, or technical knowledge. Likewise, a person
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`who would have formed design motivations as to a particular combination of known
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`elements based only on the premise that the combination may provide some benefit,
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`with no consideration of the relevance of the benefit in the specific context and in
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`relation to the costs or disadvantages of that combination, would also not have been
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`a person of ordinary skill in the art, regardless of their education, experience, or
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`technical knowledge. In my opinion, a person of ordinary skill in the art would have
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`been deliberative and considered, rather than impulsive.
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`16. Throughout my declaration, even if I discuss my analysis in the present
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`tense, I am always making my determinations based on what a person of ordinary
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`skill in the art (“POSA”) would have known at the time of the invention. Based on
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`my background and qualifications, I have experience and knowledge exceeding the
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`level of a POSA, and am qualified to offer the testimony set forth in this declaration.
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`B.
`17.
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`Burden of proof
`I understand that in an inter partes review the petitioner has the burden
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`of proving a proposition of unpatentability by a preponderance of the evidence.
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`C. Claim construction
`I understand that in an inter partes review, claims are interpreted based
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`on the same standard applied by Article III courts, i.e., based on their ordinary and
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`customary meaning as understood in view of the claim language, the patent’s
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`description, and the prosecution history viewed from the perspective of the ordinary
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`artisan. I further understand that where a patent defines claim language, the
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`definition in the patent controls, regardless of whether those working in the art may
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`have understood the claim language differently based on ordinary meaning.
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`D. Obviousness
`I understand that a patent may not be valid even though the invention
`19.
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`is not identically disclosed or described in the prior art if the differences between the
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`subject matter sought to be patented and the prior art are such that the subject matter
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`as a whole would have been obvious to a person having ordinary skill in the art in
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`the relevant subject matter at the time the invention was made.
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`20.
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`I understand that, to demonstrate obviousness, it is not sufficient for a
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`petition to merely show that all of the elements of the claims at issue are found in
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`separate prior art references or even scattered across different embodiments and
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`teachings of a single reference. The petition must thus go further, to explain how a
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`person of ordinary skill would combine specific prior art references or teachings,
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`which combinations of elements in specific references would yield a predictable
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`result, and how any specific combination would operate or read on the claims.
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`Similarly, it is not sufficient to allege that the prior art could be combined, but rather,
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`the petition must show why and how a person of ordinary skill would have combined
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`them.
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`21.
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`I understand that where an alleged motivation to combine relies on a
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`particular factual premise, the petitioner bears the burden of providing specific
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`support for that premise. I understand that obviousness cannot be shown by
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`conclusory statements, and that the petition must provide articulated reasoning with
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`some rational underpinning to support its conclusion of obviousness. I also
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`understand that skill in the art and “common sense” rarely operate to supply missing
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`knowledge to show obviousness, nor does skill in the art or “common sense” act as
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`a bridge over gaps in substantive presentation of an obviousness case.
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`III. Overview of the ’140 Patent
`22. U.S. Patent 10,971,140, titled “Speech recognition circuit using
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`parallel processors,” is directed to an improved speech recognition circuit that
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`“uses parallel processors for processing the input speech data in parallel.” Ex.
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`1001, 1:18-20. The ’140 patent teaches multiple processors “arranged in groups or
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`clusters,” with each group or cluster of processors connected to one of several
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`“partial lexical memories” that “contains part of the lexical data.” Ex. 1001, 3:13-
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`18. “Each lexical tree processor is operative to process the speech parameters using
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`a partial lexical memory and the controller controls each lexical tree processor to
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`process a lexical tree corresponding to partial lexical data in a corresponding
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`partial lexical memory.” Ex. 1001, 3:19-24. The ’140 patent further teaches that
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`the invention “provides a circuit in which speech recognition processing is
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`performed in parallel by groups of processors operating in parallel in which each
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`group accesses a common memory of lexical data.” Ex. 1001, 3:62-66.
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`IV. Replacing Jiang’s generic processor with Chen’s supercomputer
`processing architecture would not have been obvious
`23. Limitations 1(a) of claim 1 of the ’140 patent recites “one or more
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`clusters of processors, each of the one or more clusters of processors comprising: a
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`plurality of processors.” Pet. 66.
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`24.
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`I understand it is undisputed that Jiang does not teach one or more
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`clusters of processors. Pet. 14. I further understand that, to meet that requirement of
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`the challenged claims, the Petition relies on Chen. Pet. 15. In particular, I understand
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`that the Petition proposes to “substitute[]” Chen’s processing architecture, as
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`depicted in Chen’s Fig. 4 (below) in place of Jiang’s disclosed processor. Pet. 19;
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`Ex. 1004, Fig. 4 (annotated).
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`25. Chen and Jiang, however, are highly distinct references directed to
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`entirely different contexts and domains. More specifically, Jiang’s teaching is about
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`utilizing silences in speech signals to improve speech recognition, whereas Chen
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`disclosed a hardware design for general-purpose parallel computers. The Petition’s
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`combination theory appears to give no consideration to those realities.
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`Jiang’s “Preferred Embodiment” discloses “an exemplary system for
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`26.
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`implementing the invention [that] includes a general purpose computing device in
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`the form of a conventional personal computer 20, including processing unit 21. . . .”
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`Ex. 1004, 5:4-7. Jiang likewise repeatedly refers to “personal computer 20”
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`throughout its disclosure as the “exemplary system” of its invention. Ex. 1004, 5:13-
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`16, 5:27-30, 5:44-46, 5:55-58, 6:6-10, 6:12-15, 6:28-31, 6:39-42. Indeed, Jiang
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`specifically teaches: “tree search engine 74 is preferably implemented in CPU 21,”
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`which is the CPU shown for personal computer 20, “or may be performed by a
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`dedicated speech recognition processor employed by personal computer 20.” Ex.
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`1004, 6:39-42. An ordinary artisan would have known at the time that Jiang’s CPU
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`21 for a conventional personal computer would have been of the nature of the Intel
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`and AMD personal computer processors that dominated the market through the late
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`1990s and early 2000s. An ordinary artisan would have likewise understood that
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`Jiang’s reference to “a dedicated speech recognition processor” referred to another
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`processor of the same nature as the Intel or AMD processor that would have served
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`as CPU 21 in Jiang, or else a digital signal processor of the type sold by Texas
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`Instruments at the time. While Jiang teaches that CPU 21 in personal computer 20
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`“may include one or more processors,” Ex. 1004, 6:39-41, an ordinary artisan would
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`have understood that teaching to refer to the possibility of using multiple hardware
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`processors included in a conventional personal computer at the time to form CPU
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`21.
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`27. Chen, by contrast, is a patent disclosed by Cray Research, which was
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`known at the time as a leading manufacturer of high performance and extremely
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`expensive supercomputers for large-scale computational tasks such as weather
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`forecasting. Ex. 1005 at 1 (“Assignee”). Indeed, Chen expressly refers to the
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`“original supercomputer developed by the assignee of the present invention.” Ex.
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`1005, 4:4-5. Chen
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`thus
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`teaches
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`that Cray
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`itself developed
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`the original
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`supercomputer. Moreover, the Chen reference is expressly directed towards
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`improving upon the prior supercomputers developed by Cray and others. Ex. 1004,
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`4:57-5:6. Indeed, Chen states “[t]he present invention relates generally to parallel
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`processing computer systems for performing multiple-instruction-multiple-data
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`(MIMD) parallel processing.” Ex. 1004, 1:29-31. Chen further teaches that its
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`invention was directed to “a high performance parallel processing computer
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`system.” Ex. 1005, Abstract, 1:32-43, 5:9-13. An ordinary artisan would have
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`understood Chen’s reference to “a high performance parallel processing computer
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`system” and “parallel processing computer systems for performing . . . MIMD
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`parallel processing” at the time to refer to supercomputers, not conventional CPU-
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`based personal computers. Notably, a CPU (central processing unit) based
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`processing system is considered the opposite of a parallel processing system.
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`Moreover, while Chen teaches that its disclosed parallel processing computer could
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`be built using “commercially [sic] single chip microprocessors, as well as
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`commercially available memory chips,” Ex. 1005, 10:18-21, Chen’s system would
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`have nonetheless required four clusters of processors, each cluster containing at least
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`two processors, and each cluster connected to a dedicated cluster shared memory,
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`with multiple clusters also adjacently interconnected to another cluster’s memory.
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`Ex. 1005, 9:10-19. In other words, Chen’s system required at least eight processors
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`and four cluster-shared memories, as well as complex intra-cluster and inter-cluster
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`connections between the processors themselves, the processors to various memories,
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`and the memories to one another. An ordinary artisan would have understood that,
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`even using commercially available processors and memory chips, Chen’s
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`architecture would have entailed far greater cost, complexity, and hardware space
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`requirements than what would have been suitable for a conventional personal
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`computer at the time.
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`28.
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`I note that in the late 1990s and early 2000s, the costs of processors and
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`memories were generally known to be the most significant cost categories for
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`personal computers. Thus, for instance, an ordinary artisan would have understood
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`that adding seven more processors and three more memories and creating the
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`necessary interconnections to form Chen’s supercomputer architecture in the context
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`of a personal computing device would have rendered the resulting computing system
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`cost prohibitive for the general public, and thus for the vast majority of automatic
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`speech recognition customers.
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`29. Moreover, successfully modifying a standard personal computer to
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`implement Chen’s supercomputer processor and memory architecture would have
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`required a level of expertise in parallel computer design that would have exceeded
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`the qualifications that the Petition has identified for the person of ordinary skill in
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`the art. I know for a fact that ordinary artisans in the field of speech recognition,
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`many of whom I have taught and trained throughout my career, do not have the
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`expertise to design parallel computing systems of the type taught in Chen. Besides
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`processors and memories, such a design would have to address core hardware issues
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`such as registers, busses, controllers, communication protocols, and even cooling
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`fan systems. Such knowledge is not necessary or even particularly relevant to an
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`ordinary artisan in the field of automatic speech recognition. Indeed, while my own
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`qualifications would significantly exceed those of the ordinary artisan as defined by
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`the Petition, I personally do not have the expertise to implement Chen’s teachings if
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`I were asked to modify a personal computer to realize Chen’s parallel processing
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`architecture; it is worth pointing out that, in computer science and engineering,
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`parallel processing is a computer systems area that is distinct from the signal
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`processing and machine learning area where speech recognition belongs.
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`30. Given that Jiang was expressly directed to “a general purpose
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`computing device in the form of a conventional personal computer 20,” Ex. 1004,
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`5:4-7, an ordinary artisan would not have been motivated to substitute Jiang’s
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`generic “processing unit 21”/”CPU 21” with the Cray supercomputer “parallel
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`processing computer systems for performing . . . MIMD parallel processing”
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`disclosed in Chen. Ex. 1005, 1:29-31. By way of analogy, such a modification would
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`have been akin to replacing a conventional engine in a conventional automobile with
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`the engine from a Formula One car. Although such a modification would appear on
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`the surface to promise to make the conventional car faster, there is nothing obvious
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`about it, and the potential motivation of “more speed” is not ultimately a motivation
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`to make such a substitution because a Formula One engine would be fundamentally
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`unsuitable for use with a conventional automobile for the general public. In the same
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`vein, while replacing a conventional computer processor with a supercomputer
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`parallel processing architecture may superficially seem poised to make the
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`conventional computer “faster,” an ordinary artisan would have known that such a
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`substitution for the sake of speech recognition makes no sense. Simply put, an
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`ordinary artisan would not have been motivated to implement Jiang’s speech
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`recognition teachings, which were intended for implementation on a personal
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`computer, by using Chen’s eight-processor, four-memory, multiple-instruction-
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`multiple-data parallel computer. Such a combination would have been the work of a
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`highly unusual artisan using highly counterintuitive creativity, not the work of an
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`ordinary artisan with ordinary creativity.
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`31. Further, as explained above, such modifications would have
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`fundamentally altered the resulting device so that it would no longer be suitable as a
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`“personal computer” due to the resulting cost and even size of the system. The
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`resulting system would have greatly limited the usability of speech recognition
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`systems, including Jiang’s speech recognition techniques. Thus, an ordinary artisan
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`would not have been motivated to modify Jiang in order to use the hardware design
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`of Chen.
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`32.
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`In addition, as I explained above, an ordinary artisan also would not
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`have had a reasonable expectation of success in undertaking such a modification.
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`V. Using each of Chen’s shared cluster memories as an acoustic model
`memory for storing acoustic model data would not have been obvious
`33. Limitation 1(a)-(c) require that each of the plurality of processors in a
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`cluster share an acoustic model storing acoustic model data. The specification of the
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`’140 patent describes that same architecture, explaining: “[T]he present invention
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`provides a circuit in which speech recognition processing is performed in parallel by
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`groups of processors operating in parallel in which each group accesses a common
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`memory of lexical data. . . . Each processor within a group can access the same
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`lexical data as any other processor in the group. The controller can thus control the
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`parallel processing of input speech parameters in a more flexible manner. For
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`example, it allows more than one processor to process input speech parameters using
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`the same lexical data in a lexical memory. This is because the lexical data is
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`segmented into domains which are accessible by multiple processors.” Ex. 1001,
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`3:44-58 (emphasis added).
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`34. Figure 2 of the patent, annotated below, further illustrates that
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`architecture by showing two groups of lexical tree processors, with each group
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`containing multiple processors 1-k, and each group of processors connected to a
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`dedicated “acoustic model memory.” Ex. 1001, Fig. 2 (annotated).
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`35.
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`I understand that the Petition’s theory regarding limitations 1(a)-(c)
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`relies on the combination of Jiang and Chen, Pet. 14-31, 54, and in particular relies
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`on Chen as teaching groups of processors placed into “separable clusters, each
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`cluster having a common cluster shared memory that is symmetrically accessible by
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`all of the processors in that cluster.” Pet. 23.
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`36.
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`I note, however, that Chen does not contain any teachings regarding
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`speech recognition systems. Jiang, on the other hand, does not teach clusters of
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`processors with each cluster having its own dedicated acoustic model memory
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`storing acoustic model data. Accordingly, neither Chen nor Jiang suggests the
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`combination the Petition relies on.
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`18
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`37. Rather, as explained above, I do not believe any ordinary artisan would
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`have found it obvious to substitute Jiang’s conventional personal computer processor
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`and memory for the supercomputer processor and memory architecture of Chen.
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`Moreover, I see no reason why an ordinary artisan would have been motivated to
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`use each of Chen’s four cluster-shared memories as an acoustic model memory for
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`storing acoustic model data.
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`VI.
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`It would not have been obvious to configure each of Chen’s eight or more
`processors “to compute a probability” as recited in the challenged claims
`I understand the Petition alleges it would have been obvious to
`38.
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`configure each of Chen’s eight or more processors in its four clusters such that every
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`processor would be configured “to compute a probability,” as recited in limitation
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`1(c).
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`39.
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`I disagree. I see no reason why an ordinary artisan, using ordinary
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`creativity, would allocate every processor of Chen’s large computing power to
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`determining the likelihood scores recited in Jiang. I note that the Petition has not
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`identified any prior art in the field of speech recognition that allocated that level of
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`computing power “to compute a probability,” as recited in the challenged claims.
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`40.
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`I hereby declare that all statements made herein of my own knowledge
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`are true and that all opinions expressed herein are my own; and further that these
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`statements were made with the knowledge that willful false statements and the like
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`are punishable by fine or imprisonment, or both, under Section 1001 of Title 18 of
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`the United States Code.
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`DeLiang Wang, Ph.D.
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`Executed on March 15, 2023
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`20
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`EXHIBIT A
`EXHIBIT A
`
`
`
`Wang's CV, April 2022
`
`DeLiang Wang
`Department of Computer Science and Engineering
`The Ohio State University
`Columbus, OH 43210-1277
`Phone: (614) 292-6827; Fax: (614) 292-2911
`Email: dwang@cse.ohio-state.edu
`URL: "https://web.cse.ohio-state.edu/~dwang"
`
`
`
`Research Interests:
`
`Computational audition and vision, speech and audio processing, and deep learning.
`
`
`Education:
`
`1/88 – 8/91 Ph.D., Computer Science, University of Southern California, Los Angeles.
`
`
`Dissertation Advisor: Prof. Michael Arbib.
`
`9/83 – 7/86 M.S., Computer Science, Beijing (Peking) University, Beijing.
`
`
`Thesis Advisor: Prof. Zhuoqun Xu.
`
`9/79 – 7/83 B.S., Computer Science, Beijing University, Beijing.
`
`
`Professional Experience:
`
`10/91 –
`
`Professor (10/01 - )/Associate Professor (10/97 - 9/01)/Assistant Professor
`(10/91 - 9/97), Department of Computer Science & Engineering and Center for
`Cognitive and Brain Sciences (also Participating Faculty in Department of
`Biomedical Engineering), The Ohio State University, Columbus, OH.
`
`
`1/15 – 6/19 Visiting Scholar, Center of Intelligent Acoustics and Immersive Communications,
`Northwestern Polytechnical University, Xi’an, China
`
`
`10/14 – 12/14 Visiting Scholar, Starkey Hearing Technologies, Eden Prairie, MN
`
`10/06 – 6/07 Visiting Scholar, Oticon A/S, Copenhagen, Denmark
`
`10/98 – 9/99 Visiting Scholar, Department of Psychology, Harvard University, Cambridge,
`MA
`
`
`Summer 1995 Visiting Research Fellow, Department of Computer Science, University of
`
`
`Sheffield, Sheffield, U.K.
`
`
`1/88 – 8/91 Research Assistant, Department of Computer Science, University of Southern
`
`
`California, Los Angeles, CA.
`
`Summer 1990 Visiting Scholar, Department of Neurobiology, University of Kassel, Kassel,
`
`1
`
`
`
`Wang's CV, April 2022
`
`
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`Germany.
`
`
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`8/86 – 12/87 Assistant Research Fellow, Institute of Computing Technology, Academia Sinica,
`
`
`Beijing.
`
`Spring 1985 Teaching Assistant, Computer Science Department, Beijing University, Beijing.
`
`
`Awards and Honors:
`
`National Science Foundation Research Initiation Award, 1992
`
`Office of Naval Research Young Investigator Award, 1996
` One of 34 Young Investigators selected from 416 contenders across science and engineering
`disciplines for "exceptional promise for doing creative research"
`
`
`OSU College of Engineering Lumley Research Award, 1996, 2000, 2005, 2010
`
`IEEE Fellow, 2004
`“For contributions to advancing oscillatory correlation theory and its application to auditory
`and visual scene analysis”
`
`IEEE Computational Intelligence Society Outstanding Paper Award, 2007
`
`For Paper J54 published in the 2005 IEEE Transactions on Neural Networks
`
`International Neural Network Society Helmholtz Award, 2008
`
`IEEE Distinguished Lecturer, 2010-2012
`
`OSU Distinguished Scholar Award, 2014
`
`Starkey Signal Processing Research Award, 2015
`
`For Paper C155 presented in the 2015 International Conference on Acoustics, Speech, and
`Signal Processing
`
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`IEEE Signal Processing Society Best Paper Award, 2019
`
`For Paper J109 published in the 2014 IEEE/ACM Transactions on Audio, Speech, and
`Language Processing
`
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`International Neural Network Society Ada Lovelace Service Award, 2020
`
`ISCA Fellow, 2021
`“For seminal work in the area of speech separation and speech segregation”
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`
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`Professional Services:
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`Co-Editor-in-Chief: Neural Networks (Elsevier, 2011-)
`
`2
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`
`
`Wang's CV, April 2022
`
`
`Editorial Board Membership:
`• Cognitive Neurodynamics (Springer, 2006-2017)
`IEEE Transactions on Neural Networks (1998-2006)
`•
`•
`IEEE Transactions on Audio, Speech, and Language Processing (2012-2015)
`• Neurocomputing (Elsevier Science, 1995-2010)
`• Neural Computing & Applications (Springer, 1997-2017)
`• EURASIP Journal on Audio, Speech, & Music Processing (2006-2016)
`• Neural Networks (Elsevier, 2009-2010)
`• Frontiers in Electrical and Electronics Engineering (Springer, 2010-2013)
`• Cognitive Computation (Advisory, Springer, 2012-)
`
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`Guest Editor for Photogrammetric Engineering & Remote Sensing, the official journal of the
`American Society for Photogrammetry and Remote Sensing, for a special issue on Target
`Recognition (1999).
`
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`Editorial Advisory Board Membership: Handbook of Brain Theory and Neural Networks, 2nd
`Ed., 2003, MIT Press.
`
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`Guest Co-Editor for Neural Networks Special Issue on Progress in Neural Network Research,
`2003.
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`Guest Editor for IEEE Transactions on Neural Networks Special Issue on Temporal Coding for
`Neural Information Processing, 2004.
`
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`Organizer: International Neural Network Society Special Interest Group on Neurodynamics and
`Chaos (1994-1996).
`
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`Co-Chair: Working Group III/5: "Remote sensing and vision theories for automatic scene
`interpretation" of the International Society for Photogrammetry and Remote Sensing (1996-
`2000).
`
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`Chair: IEEE Computational Intelligence Society Neural Networks Technical Committee (2004).
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`President: International Neural Network Society (2006).
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`Member: Governing Board of the International Neural Network Society (2003-2005; 2008-2013;
`2020-2022); IEEE Computational Intelligence Society Fellows Committee (2008-2009);
`IEEE Computational Intelligence Society Neural Networks Technical Committee (2005-
`2006, 2012-2018); IEEE Neural Network Society Neural Networks Technical Committee
`(2000-2003); IEEE Signal Processing Society Machine Learning for Signal Processing
`Techn
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