Philip N. Garner

2.2k citations
139 papers · 1.5k indexed · h-index 22
Topics
Speech Recognition and Synthesis (85 papers)Speech and Audio Processing (75 papers)Music and Audio Processing (48 papers)

In The Last Decade

Philip N. Garner

128 papers receiving 1.3k citations

Peers

Philip N. Garner
Comparison fields: 5 of 85
  • Artificial Intelligence 1.0k
  • Signal Processing 922
  • Experimental and Cognitive Psychology 227
  • Computational Mechanics 156
  • Electrical and Electronic Engineering 118
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Citations per field
00.5×4.3×
Oliver Watts · 1×
Citations per year

Countries citing papers authored by Philip N. Garner

Since Specialization
Citations

This map shows the geographic impact of Philip N. Garner's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Philip N. Garner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip N. Garner more than expected).

Fields of papers citing papers by Philip N. Garner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Philip N. Garner. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Philip N. Garner. The network helps show where Philip N. Garner may publish in the future.

Co-authorship network of co-authors of Philip N. Garner

This figure shows the co-authorship network connecting the top 25 collaborators of Philip N. Garner. A scholar is included among the top collaborators of Philip N. Garner based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Philip N. Garner. Philip N. Garner is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 1
2 12
3 2
4
A Variational Prosody Model for the decomposition and synthesis of speech prosody.
2
5 6
6 4
7 20
8 4
9
Learning SQL in steps
5
10 20
11 7
12 1
13
Convolutional Pitch Target Approximation Model for Speech Synthesis
1
14 14
15
A Just-in-Time Document Retrieval System for Dialogues or Monologues
1
16
A Speech-based Just-in-Time Retrieval System using Semantic Search
6
17
Personalising Speech-To-Speech Translation in the EMIME Project
15
18
Speaker adaptation and the evaluation of speaker similarity in the EMIME speech-to-speech translation project
19
19
A MAP Approach to Noise Compensation of Speech
0
20 6

About Philip N. Garner

Philip N. Garner is a scholar working on Signal Processing, Artificial Intelligence and Experimental and Cognitive Psychology, having authored 139 papers that have together received 1.5k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (85 papers), Speech and Audio Processing (75 papers) and Music and Audio Processing (48 papers). The work is most often cited by research in Signal Processing (922 citations), Artificial Intelligence (1.0k citations) and Experimental and Cognitive Psychology (227 citations). Philip N. Garner has collaborated with scholars based in Switzerland, United Kingdom and Germany. Frequent co-authors include Hervé Bourlard, Alexandros Lazaridis, John Dines, Petr Motlíček, David Imseng, Miloš Cerňak, Wendy Holmes, Lakshmi Babu Saheer, Afsaneh Asaei and John Holmes. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and The Leadership Quarterly.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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